In case you are unfamiliar with Karpathy's Loop[1], it is a genetic algorithm[2] where the genetic "mutations" are clever-but-random ideas generated by an LLM agent, aimed at improving a system.
(1) Let the LLM randomly perturbate the system.
(2) Measure the system's performance.
(3a) If the perturbation improved performance, keep the change.
(3b) Otherwise, don't.
(4) Repeat
In the USA, the available supply of gasoline is normally 21 or 22 days. The press occasionally trumpets this number, and people react with horror. But, hey, it's just normal. IDK about Europe, but 6 weeks of jet fuel does not surprize me.
"Before you start, please ask me any questions you have about this so I can give you more context. Be extremely comprehensive."
(I got the idea from a Medium article[1].) The LLM will, indeed, stop and ask good questions. It often notices what I've overlooked. Works very well for me!
All the Python-based functionality of this project can now be handled by the mcptools package[1]. That is, mcptools can field MCP requests and dispatch to R code; no need for an intermediate layer of Python. I wonder if the author knows about mcptools? Or did he start coding before it was available?
[1] https://github.com/karpathy/autoresearch
[2] https://en.wikipedia.org/wiki/Genetic_algorithm