Building structured memory for the agentic era.

I'm building Neotoma, a structured memory layer for AI agents. The core problem: agents are increasingly stateful—handling tasks, contacts, transactions, and commitments over time—but their memory is built for retrieval, not truth. Neotoma treats personal data the way production systems treat state: typed entities, stable IDs, full provenance, deterministic queries. Local-first, cross-platform via MCP, and entirely user-controlled.

The principle underneath is the same one that's driven all of my work: people should control their own data, memory, and digital infrastructure—not rent it from platforms that optimize for engagement over truth.

I work as a solo founder in Barcelona, operating with AI agents as a team rather than as tools. Every workflow runs through a shared repo and a shared source of truth.

Before this chapter, I spent nearly two decades building products across consumer web, crypto, and startups: writing and shipping at TechCrunch, co-founding Plancast (acquired by Active Network), co-founding KITE Solutions, advising and building with early-stage startups, leading user experience at Hiro for the Stacks blockchain, and running Leather at Trust Machines. You can see the full arc on my timeline.

Currently:

• Neotoma — user-owned memory layer for AI agents (MCP, structured data, provenance)
• Ateles — personal operating system backed by that layer (truth → strategy → execution)
• markmhendrickson.com — essays and updates (this repo builds the site)

Elsewhere: Website · X @markymark · LinkedIn