Ohh, looks interesting, thanks for the tip!

Git as db is clever, and the sqlite cache is nice. I'd been sketching sqlite based memory features myself. So much of the current ecosystem is suboptimal just because it's new. The models are trained around immutable conversation ledgers with user/tool/assistant blocks, but there are compelling reasons to manipulate both sides at runtime and add synthetic exchanges. Priming with a synthetic failure and recovery is often more effective than a larger, more explicit system message. Same with memory, we just haven't figured out what works best.

For code agents specifically, I found myself wanting old style completion models without the structured turn training--doesn't exist for frontier models. Talking to Claude about its understanding of the input token stream was fascinating; it compared it to my visual cortex and said it'd be unreasonable to ask me to comment on raw optic nerve data.

"Tell it to reread it again"-exactly the problem. My bookkeeping/decision engine has so much documentation I'm running out of context, and every bit is necessary due to interconnected dependencies. Telling it to reread content already in the window feels wrong, that's when I refine the docs or adjust the framework. I've also found myself adding way more docstrings and inline docs than I'd ever write for myself. I prefer minimal, self-documenting code, so it's a learning process.

There's definitely an art to prompts, and it varies wildly by model, completely different edge cases across the leading ones. Thanks again for the tip; I suspect we'll see a lot of interesting memory developments this year.

I am working on a strict sqlite alternative to Beads as an experiment so far it works.