thald

Another approach is to redesign CI pipelines. Most checks should be done within the agent loop for quicker feedback (using hooks, skills). Linting, unit tests etc.

CI should handle only stuff which cannot be run locally due to resource or setup reasons.

Also CI should only receive pre-validated change candidates with passed checks inside the agent loop.

I was working some time ago on LLM assisted optimizations and algorithm discovery and this does not look like a novel idea.

AlphaEvolve from google is evolutionary algorithm which uses LLMs for Idea generation following very similar loop:

- https://deepmind.google/blog/alphaevolve-a-gemini-powered-co...

- Open source implementation of the algorithm: https://github.com/algorithmicsuperintelligence/openevolve

Interesting experiment. Looking at this I immediately thought similar experiment run by Google: AlphaEvolve. Throwing LLM compute at problems might work if the problem is well defined and the result can be objectively measured.

As for this experiment: What does quality even mean? Most human devs will have different opinions on it. If you would ask 200 different devs (Claude starts from 0 after each iteration) to do the same, I have doubts the code would look much better.

I am also wondering what would happen if Claude would have an option to just walk away from the code if its "good enough". For each problem most human devs run cost->benefit equation in their head, only worthy ideas are realized. Claude does not do it, the code writing cost is very low on his site and the prompt does not allow any graceful exit :)