exactly, i find the article a wierd take. i would have thougt that being able to catch errors at compile time is the assurance that the LLM generated code is actually decent.

so does this mean that the LLM writes code that is so good that the compiler does not find any more errors?

or is it due to the nature of haskell that makes it hard to write bad code to begin with?

or just that because the haskell compiler catches more errors there is less broken haskell code for the AI to train on?

and what does that mean for the switch to python? if the python compiler/interpreter doesn't catch as many errors do we even know that the code is good?

or is this more like the belief if the LLM can generate good haskell code, surely it can also generate good python?

what's the solution here? speeding up the haskell compiler? if that were easy, would it not already have happened?

personally i still don't trust LLM code generation. i didn't learn haskell yet, but what i hear about it makes me more likely to trust that LLMs can generate good haskell code than python.

i believe the future in LLM code generation is code that can be proven to be correct. proving code correct has been a research topic at some point.