marciopugaShow HN: A plain-text cognitive architecture for Claude Code
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.
This is really interesting. At this point you seem to be modelling real human memory
In my opinion, this should happen inside the LLM dorectly. Trying to scaffold it on top of the next token predictor isnt going to be fruitful enough. It wont get us the robot butlers we need.
But obviously thays really hard. That needs proper ML research, not primpt engineering
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.