I'm a biologist and software developer. PhD in genetics, and ~20 years building software products. So I think I have a different view on things like memory. My thoughts on how memory with a coding agent should work:

Tuesday morning. New session.

I type: "What did we do last Tuesday?" : LLM tells me: the refactoring, the bug in the auth middleware, the decision to switch to connection pooling. I ask: "What was still open?" : LLM shows me. I ask: "Why did we stop?" : LLM explains: you hit a dependency issue, decided to wait for the upstream fix. I ask: "What did you think about that approach?" : LLM gives me its honest assessment with deep details from last week's context, not a guess.

This is what I expect from an intelligent Coding Agent. Not because it stored a few preferences about me. Because the project itself still has continuity: decisions, blockers, dead ends, open work, code context, and the reasoning behind all of it. But back in December it wasn't that way, not much better now. So I changed it for me. I built YesMem with Claude.

The hard part was: can the agent still find the old rationale, the half-finished plan, the abandoned approach, the bug we promised never to repeat, and the reason we stopped? With YesMem, a new session does not feel like a reset. It feels like a return.

YesMem is a memory system (and really much more) for AI coding agents built on how biology actually works: filter at encoding, consolidate during downtime, update on every recall, forget on purpose. Single Go binary, no cloud, only local. Works with Claude Code (also OpenCode and Codex). Not RAG with a different name, structured memory that gets sharper every session. LoCoMo Benchmark 0.87.

So how does this work? Here are 4 Points (out of >30) which together make YesMem unique in my point of view. Enjoy.

1. The context window stops rotting. Your brain does not let everything into awareness. It filters at the gate, suppresses noise, keeps what matters conscious. YesMem runs an HTTP proxy that does the same: tool results get stubified, stale content collapses, cache breakpoints are optimized. 91-98% cache hit rates, adjustable per session. The important project state survives.

2. Rules that hold. CLAUDE.md comes with a disclaimer: "This context may or may not be relevant." Claude Code itself tells the model it is optional. YesMem has pattern matching and a guard LLM that evaluates every tool call before execution. If the agent tries something you said never to do, blocked. Plus it changes the system prompt to NOT ignore CLAUDE.md.

3. Memory that gets sharper, not staler. A trust hierarchy (user_stated > agreed_upon > llm_suggested > llm_extracted), forked agents that extract learnings live during a session, and a consolidation pipeline that deduplicates and clusters after sessions end. Memories get scored, superseded when outdated, decayed when unused. Your next session is sharper than your last.

4. Your system prompt, not theirs. Every AI coding agent ships with a…

为什么值得关注

能改变理解方式，而不只是重复常识；符合当前抓取需求；它提供了新的理解或解释，而不只是表面观点

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