yep it's public: https://github.com/rodspeed/epistemic-memory

The observations layer being append-only is smart, thats basically the same instinct as the tensions log. The raw data stays honest even when the interpretation changes.

The freshness approach and explicit confidence scores probably complement each other more than they compete. Freshness tells you when something was last touched, confidence tells you how much weight it deserved in the first place. A belief you inferred once three months ago should decay differently than one you confirmed across 20 sessions three months ago. Both are stale by timestamp but they're not the same kind of stale.

You're probably right long term. If LLMs eventually handle memory natively with confidence and decay built in, scaffolding like this becomes unnecessary. But right now they don't, and the gap between "stores everything flat" and "models you with any epistemological rigor" is pretty wide. This is a patch for the meantime.

The other thing is that even if the model handles memory internally, you probably still want the beliefs to be inspectable and editable by the user. A hidden internal model of who you are is exactly the problem I was trying to solve. Transparency might need to stay in the scaffold layer regardless.

I'm one of those zero star repos. I've been using Claude Code for some weeks now and built a personal knowledge graph with a reasoning engine, belief revision, link prediction. None of it is designed for stars, its designed for me. The repo exists because git is the right tool for versioning a system.. that evolves every day.

The framing assumes github repos are supposed to be products.

I've been building persistent memory for Claude Code too, narrower focus though: the AI's model of the user specifically. Different goal but I kept hitting what I think is a universal problem with long-lived memory. Not all stored information is equally reliable and nothing degrades gracefully.

An observation from 30 sessions ago and a guess from one offhand remark just sit at the same level. So I started tagging beliefs with confidence scores and timestamps, and decaying ones that haven't been reinforced. The most useful piece ended up being a contradictions log where conflicting observations both stay on the record. Default status: unresolved.

Tiered loading is smart for retrival. Curious if you've thought about the confidence problem on top of it, like when something in warm memory goes stale or conflicts with something newer.