While I agree with your sentiment, I just want to say that if your approach is to have the LLM read every file into context, or you're working in some gigantic thread (using the million token capacity most frontier models have) that's really not the best way to do it.

Not even a human would work that way... you wouldn't open 300 different python files and then try to memorize the contents of every single file before writing your first code-change.

Additionally, you're going to have worse performance on longer context sizes anyways, so you should be doing it for reasons other than cost [1].

Things that have helped me manage context sizes (working in both Python and kdb+/q):

- Keep your AGENTS.md small but useful, in it you can give rules like "every time you work on a file in the `combobulator` module, you MUST read the `combobulator/README.md`. And in those README's you point to the other files that are relevant etc. And of course you have Claude write the READMEs for you...

- Don't let logs and other output fill up your context. Tell the agent to redirect logs and then grep over them, or run your scripts with a different loglevel.

- Use tools rather than letting it go wild with `python3 -c`. These little scripts eat context like there's no tomorrow. I've seen the bots write little python scripts that send hundreds of lines of JSON into the context.

- This last tip is more subjective but I think there's value in reviewing and cleaning up the LLM-generated code once it starts looking sloppy (for example seeing lots of repetitive if-then-elses, etc.). In my opinion when you let it start building patches & duct-tape on top of sloppy original code it's like a combinatorial explosion of tokens. I guess this isn't really "vibe" coding per se.

[1] https://arxiv.org/html/2602.06319v1