[anonylizard]

LLMs massively compound the advantage of existing popular languages, namely python. Any new learner will find it infinitely easier to use sonnet 3.5 to overcome the so called '2 language barrier' for python, while the lacking data for Julia becomes the real barrier.

This issue will remain until LLMs get so smart they can maybe self-iterate and train on a given language. By then though, we'd likely get languages designed and optimized for LLMs.

[samatman]

To back up the sibling comment, I've found ChatGPT quite capable where Julia is concerned. It does hallucinate the occasional standard library function, but a) it gets it right after it's told it was wrong about half the time and b) Julia's documentation is fairly good, so finding what that function is really called is not a big deal.

It can even debug Pkg/build chain problems, which... Julia could use a bit of polish there. On paper the system is quite good, but in practice things like point upgrades of the Julia binary can involve a certain amount of throwing spaghetti at the wall.

[minetest2048]

There is a paper that claims that Julia performs the best in ChatGPT: https://arxiv.org/abs/2308.04477

Chris blog on that: https://www.stochasticlifestyle.com/chatgpt-performs-better-...

[SatvikBeri]

For what it's worth I've found Claude Sonnet to work really well with Julia.

One fun exercise was when a friend handed me a stack of well-written, very readable Python code that they were actually using. They were considering rewriting it in C, which would have been worth it if they could get a 10x speedup.

I had Sonnet translate it to Julia, and it literally ran 200x faster, with almost identical syntax.

[moelf]

More fragmented, calling-some-api based python code actually causes LLM to hallucinate and mix libraries more: https://www.stochasticlifestyle.com/chatgpt-performs-better-...