I used to think the goal was to make AI remember everything. Then I watched an agent drown in its own backlog: every past conversation became noise, and the useful signal got buried.
That is the trap of infinite memory. A memory system that never forgets is just a slower search engine.
The fix is to copy what biology already solved — a forgetting curve. Not random loss, but structured forgetting: keep recent context hot, compress the middle into durable facts, and let the truly irrelevant decay. Our local memory layer does exactly this — short-term records get consolidated into long-term facts, and the rest fades on a schedule.
Why local? Because the forgetting decisions are yours. Nothing uploads to a cloud you do not control. The model runs on your machine, the memory stays on your disk.
I am building HyperMarrow, a local-first memory system for AI agents and coding assistants. If you want to see how a forgetting curve feels in practice, the download and docs are at the link in my profile (?from=devto).
What would you automate if your AI actually remembered the right things?
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