Ive been amazed at how well LLMs are at writing Gleam[1] and Lustre[2]. Compared to a mainstream language, there is basically zero gleam code in the training data.

I have no evidence to back this up, but I suspect that languages that are good for humans[3] will be good for LLMs. Compiled, strongly typed, statically typed, immutable, pure functions, pattern matched, memory safe, etc.

[1] https://gleam.run [2] https://lustre.hexdocs.pm [3] Yes I realize that languages features that are "good for humans" is a hotly debated topic. That's just my personal list for what I like in a language.

I’ve been developing a language for a few years, and even with incomplete semantics and a simple one page example LLMs don’t have much trouble writing it.

I think the language/syntax has an impact, but the tooling around it will be most important for LLMs, in the same way it is for humans.

Gleam has been stable for over two years, so maybe it's been long enough that LLMs have internalised the documentation.

Given it's a language that doesn't really contain any groundbreaking ideas[1] (the closest is 'use' IMO), it's possible LLMs can reuse patterns from other functional language.

[1] This isn't criticism. I love how Gleam turned out.

That's not a take I was expecting to find here. I've found most LLMs absolutely dreadful when it comes to Gleam, to the point that I most often disable even inline autocomplete when working in Gleam codebases.

Too often I find them getting pulled into larger ruts in the training data and trying to insert language features that don't exist (ifs, loops, and syntactic constructs) from more popular languages like TypeScript and Rust. Do you not experience other languages getting partially substituted in when you have LLMs write Gleam?