That's what I'm thinking too. There is a lot of noise and I know teams where the majority of the people writing Python just have no idea what they're doing.

I'm working with Clojure which is used mostly by senior engineers and it still blows my mind how well Claude writes software in it even though it's a fringe language. It's even able to pick up in-house DSLs written with macros.

Having used Python on and off for 20 years, my experience with LLMs writing Python has been mixed. I don’t think that’s necessarily because of a low-quality dataset, but rather because Python’s applications are so broad and the language has gone through several paradigm shifts over time: sync vs. async, typed vs. untyped, scientific Python looking very different from web application code, some people really wishing it were an FP language, and others doing the clean-architecture OOP onion soup. It has gotten so fragmented.

Recently, I had a more pleasant experience using LLMs with Go. It reminds me a bit of Python 2.x, when the community seemed, in my view, more focused on embracing a stupid simple language, with everyone trying to write roughly similar "Pythonic" code.

That was the hardest part of learning PHP, all the code examples online were just awful.

I was (pleasantly) surprised by Claude Code doing Raku - also with a limited training set (~2000 Stack Overflow, a bunch of Rosetta, 2,500 modules). I put this down to the quality of the code for the core community who are all frankly uber-gremlins.

Reminds me of the time I asked Claude to write some Wordpress code for me. The results were…rough.

All my vibe coded projects (personal) are Go backend services, with Typescript/React frontend. And my thoughts were based on similar things. Like why I wouldn't use PHP for that, either.

There's a broken idea that AI know Python because they're written in Python.

Not how any of it works.