The reason it works so well is that everyone’s “personal unique language” really isn’t all that different from what’s been proposed before, and any semantic differences are probably not novel. If you make your language C + transactional memory, the LLM probably has enough information about both to reason about your code without having to be trained on a billion lines.

Probably if you’re trying to be esoteric and arcane then yeah, you might have trouble, but that’s not normally how languages evolve.

My comment is based precisely on using these tools frequently, if not daily, so what's wild is you assuming I don't.

The impact that lack of training data has on the quality of the results is easily observable. Try getting them to maintain a Python codebase vs. e.g. an Elixir one. Not just generate short snippets of code, but actually assist in maintaining it. You'll constantly run into basic issues like invalid syntax, missing references, use of nonexistent APIs, etc., not to mention more functional problems like dead, useless, or unnecessarily complicated code. I run into these things with mainstream languages (Go, Python, Clojure), so I don't see how an esolang could possibly fair any better.

But then again, the definitions of "just fine" and "decent" are subjective, and these tools are inherently unreliable, which is where I suspect the large disconnect in our experiences comes from.