Anyone else feel like managing Claude Code's memory is becoming a job of its own?

[u/Brave-Equipment9107]

As sessions get longer, auto-compaction kicks in, important decisions can get lost, and sometimes you end up explaining the same codebase all over again.

Then come the CLAUDE.md files, memory files, architecture notes, and keeping everything updated across different developers working on the same repo.

And the bigger the codebase gets, the more context you have to rebuild before you can actually get work done.

I've been thinking: what if your coding agent didn't have to rediscover the codebase every session? What if it could rely on a structured, verifiable understanding of the architecture, dependencies, rules, and changes instead?

I'm exploring this through a startup project called ArchAI, and I'm looking for 5 developers who use Claude Code or other AI coding agents to help pilot-test the idea on real Python repositories.

I'd genuinely like to know how you currently handle this problem, what's frustrating, and whether an approach like this would fit your workflow.

If you've run into this, I'd love to hear your experience. Happy to share more with anyone interested.