Local, private AI memory is where this whole space is headed — but the interfaces are still a mess. Everyone builds their own silo, and switching costs lock users in.
I have been building HyperMarrow, a local-first memory system with four building blocks (recall / intercept-record / consolidation / file-bridge), exposed over MCP so the data stays on the user's machine.
I am looking for a few technical co-builders who care about this problem: people who want to help shape how local memory is structured, shared, and made interoperable — not just ship another closed product.
What is on the table: architecture decisions, the open spec, and the hard parts (privacy boundaries, forgetting curves, cross-agent sharing). If that sounds like your kind of problem, the docs and contact path are here: https://hm.qianshi.cool/api/v2/dl?from=devto
What would make a local-memory standard actually stick for you?
(Disclosure: I build HyperMarrow, the local-first memory system described above — that is also why I am recruiting co-builders.)
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