Comment by pjerem
6 hours ago
If you use OpenCode (open source Claude Code implementation), you can configure compaction yourself : https://opencode.ai/docs/en/config/#compaction
6 hours ago
If you use OpenCode (open source Claude Code implementation), you can configure compaction yourself : https://opencode.ai/docs/en/config/#compaction
OpenAI has some magic they do on their standalone endpoint (/responses/compact) just for compaction, where they keep all the user messages and replace the agent messages or reasoning with embeddings.
> This list includes a special type=compaction item with an opaque encrypted_content item that preserves the model’s latent understanding of the original conversation.
Some prior discussion here https://openai.com/index/unrolling-the-codex-agent-loop/
Not sure if it's a common knowledge but I've learned not that long ago that you can do "/compact your instructions here", if you just say what you are working on or what to keep explicitly it's much less painful.
In general LLMs for some reason are really bad at designing prompts for themselves. I tested it heavily on some data where there was a clear optimization function and ability to evaluate the results, and I easily beat opus every time with my chaotic full of typos prompts vs its methodological ones when it is writing instructions for itself or for other LLMs.
You can also put guidance for when to compact and with what instructions into Claude.md. The model itself can run /compact, and while I try to remember to use it manually, I find it useful to have “If I ask for a totally different task and the current context won’t be useful, run /compact with a short summary of the new focus”
so you have to garbage collect manually for the AI?
also, i don't want to make a full parent post
1M tokens sounds real expensive if you're constantly at that threshold. There's codebases larger in LOC; i read somewhere that Carmack has "given to humanity" over 1 million lines of his code. Perhaps something to dwell on