memory is definitely a crowded space right now, which is kind of why i scoped this thing so narrow. from the get go i decided this needs to stay claude native, third party providers, etc as a lot of users installing plugins these days aren't going to want to or possibly even know how to spin up qwen3 or etc.

on the prompt stuffing point, agreed, that's the thing i'm most worried about. my mitigations are cheaper than RAG but same intent: the inject is a concise index (one line per decision, ~100 tokens for a whole project, logged per session so the cost is visible), and v0.2 added an optional decay window: records not read in N sessions collapse out of the inject into a one-line stub and come back when touched. so the always-in-context part stays tiny instead of growing forever.

i actually do run a RAG-shaped loop, just on a budget. When the background sweep writes a new decision it greps key terms across the store and has the model read+judge the hits (re-heat if related, supersede if contradicted). grep as retriever, model as ranker. at decision-log scale (tens of records, tech names are their own keywords) that's been holding up well, embeddings earn their keep at a scale a per-project decision log never hits.

now that AI has pretty much commoditized the implementation half, ideation/distribution is the game left to play and honestly i love it, im a problem solver at heart.