Show HN：Slowave——为编码智能体打造的本地自适应记忆

事件

I started building Slowave because I kept running into the same problem with coding agents: every new session has the codebase and some documentation but not the context behind it, decisions that brought you there and especially the thinking process behind the code. Most memory system solutions focus primarily on the storage and retrieval aspects (vector search/RAG/graphs/ Markdown files, etc.). After months of storing memories (coding 8+ hours a day produces a lot of memories) these might hallucinate your reasoning model and they can clutter your context window. To treat semantic relationship, such as contradiction, supersession, etc, most systems added an extra LLM layer that summarize memories and continuously evaluate their semantic relevance. That comes with a non-negligible cost and introduces a split-brain system, where a second model is making decisions about memory independently of the agent actually using it. I started looking up into how human brain works, and the first thing striking me was that retrieval is just a part of the whole memory problem. Brain memories are a constant flow of information where what matters gets reinforced, what doesn't decays over time. What really matters for an efficient memory system is to retrieve memories that actually help (a human or an agent) to achieve its current task or goal given the current context. Everything else should be treated as noise. Slowave is my attempt to approach this problem differently: It

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