[levocardia]

Interesting...my experience has been that LLMs are generally better at more common languages (not surprising: more data exists in those languages!). So, my admittedly amateur vibe coding experiences have been best in Python and pretty vanilla web development setups. When I push LLMs to, say, fit advanced statistical models in R, they fall apart pretty badly. Yet they can crush a PyTorch or SciKitLearn task no problem.

[reflectiveattn]

This. This is the most important thing to consider: the available corpus the model was trained on. Remember that LLMs are inferring code. They don't "know" anything at all about its axiomatic workings. They just know what "looks right" and what "looks wrong". Agentic and RL are about to make this philosophy obsolete on grand scale, but signs still don't look good for being any to improve how much they can "hold in their head" to infer what token to spit out next from the vector embedding, tho.