Maintaining the memory is a considerable burden, and make sure that simple "fix this linting" doesn't end up in the memory, as we always fix that type of issue in that particular way. That's also the major problem I have with ChatGPT's memory: it starts to respond from the perspective of "this is correct for this person".
I am curious who sees the benefits of the memory in coding? Is it like "learns how to code better" or it learns "how the project is structured". Either way, to me, this sounds like an easy project setup thing.
pacjam•32m ago
If you're a Claude Code user (I assume much of HN is) some context on Letta Code: it's a fully open source coding harness (#1 model-agnostic OSS on Terminal-Bench, #4 overall).
It's specifically designed to be "memory-first" - the idea is that you use the same coding agents perpetually, and have them build learned context (memory) about you / your codebase / your org over time. There are some built-in memory tools like `/init` and `/remember` to help guide this along (if your agent does something stupid, you can 'whack it' with /remember). There's also a `/clear` command, which resets the message buffer, but keeps the learned context / memory inside the context window.
We built this for ourselves - Letta Code co-authors the majority of PRs on the letta-code GitHub repo. I personally have been the same agent for ~2+ weeks (since the latest stable build) and it's fun to see its memory become more and more valuable over time.
LMK if you have any q's! The entire thing is OSS and designed to be super hackable, and can run completely locally when combined with the Letta docker image.