marv1nnnnn

never heard of this one! sounds really interesting

I just fixed the local version.. My bad, totally missed it.If you are still interested, could try it again. About the scrping step, this project using crawl4ai to scrape. Suppose the url is https://xxx/yy/, it will only scrape https://xxx/yy/*. You could post it as a github issue, will try to fix it.

I think it's really a thing about reasoning model. Non-reasoning model struggles at math too. It's more like a protocol between two math genius, they could communicate with really abstract stuff

haha, is it that obvious? I only let LLM polished this one. I am not native speaker and I was trying to be polite ^-^

Thanks for your comments! Really helpful. I have seen some llms.txt like this: https://docs.agno.com/llms.txt which I don't think will help LLM in real tasks. Some background of this project is I think LLM performs better in abstraction than human do. Those AIME test scores are not joking.So maybe smart LLM don't have to communicate with another LLM in plain text, they have more efficient way to communicate. About the excessive token of LLM reasoning, I find it varies. Gemini 2.5 pro is really an overthinker, but claude 3.7 won't. Finaly, I think most vibe-coding task don't require deep understanding of how a package works. It's more like a information retrieval task, so a lot could be compressed.

Thanks for the feedback. This will be my next step. Personally I feel it's hard to design those test cases (by myself)

the description is here: https://github.com/marv1nnnnn/llm-min.txt/blob/main/sample/c...

Agree, actually this approach isn't even possible without the birth of reasoning LLM. In my test, reasoning LLM perform much better than non-reasoning LLM in interpreting the compressed file. Those LLMs are really good at understanding abstraction.

Oof, you nailed it. Thanks for the sharp eyes on llm_min_guideline.md. That's a clear sign of me pushing this out too quickly to get feedback on the core concept, and I didn't give the supporting docs the attention they deserve. My bad. Cleaning that up, and generally adding more polish, is a top priority. Really appreciate you taking the time to look deeper and for the encouragement to keep going. It's very helpful!

Honestly it's really funny. I have the initial idea, and then brainstorm with gemini 2.5 pro a lot, let it design the system. (And in prompt let it think like Jeff Dean and John Carmack ) But most version fails. Then somehow realized I can't let it design from scratch, I give gemini a structure I think is reasonable and efficient after seeing all those versions, let it polish based on that and it works much better.

I totally agreed with your critic. To be honest, it's even hard for myself to evaluate. What I do is select several packages that current LLM failed to handle, which are in the sample folder, `crawl4ai`, `google-genai` and `svelte`. And try some tricky prompt to see if it works. But even that evaluation is hard. LLM could hallucinate. I would say most time it works, but there are always few runs that failed to deliver. I actually prepared a comparison, cursor vs cursor + internet vs cursor + context7 vs cursor + llm-min.txt. But I thought it was stochastic, so I didn't put it here. Will consider add to repo as well