Read the first few comments and surprised I didn’t see it, but training data. The voluminous amount of Python in the training data.

I could write in brainfuck with ai, but I presume, wouldn’t get the same results than if going with python.

My follow up question: with AI now, why care about a lang until you need to?

Surprisingly, LLMs are actually much worse at reasoning in Python than other common programming languages for agentic coding tasks.

Data here: https://gertlabs.com/rankings?mode=agentic_coding

Just use Go. LLMs have seen a ton of it, they write it well, it compiles practically instantly, and it has all the advantages of a typed compiled language.

I created a big Python codebase using AI, and the LLM constantly guesses arguments or dictionary formats wrong. Unit tests and stuff like pydantic help, but it's better to avoid that whole class of runtime errors altogether.

That would matter if we were asking the AI to generate code open-loop: someone probably already wrote something close to what you asked for in Python. But if the agent generates code, tries to compile it, sees the detailed error messages and acts on those messages to refine the code, it's going to produce a higher quality result. rustc produces really good diagnostics. And there's a lot of Rust code online now, even if there's so much more Python and Javascript/Typescript.

I built a programming language, and LLMs can code phenomenally well in it.

I don't think the training set matters that much, since there's no way they have my language in their training set!

Programming languages have a lot in common. Python is kind of odd when it comes to languages.

Seems to me these LLMs have a critical mass of Python training data and Rust training data, so there's no advantage for Python there.

So as the article points out, an iterative process that catches the mistakes at compile time is much more suited for an AI than one that catches them at runtime.

Training data can't be the whole answer. LLMs are really good at translating to different programming languages. This makes sense, given that they are derived from text translation systems. I'm getting great results in languages with comparatively small bodies of freely available code. The bigger hurdle is usually that LLMs tend to copy common idioms in the target language and if it is an "enterprise-y" language like Java or C#, the amount of useless boilerplate can skyrocket immediately, which creates a real danger that the result grows beyond the usable context window size and the quality suffers.

Admittedly, I have very little experience with LLM-assisted Python. However, based on the severe degradation in output quality I have seen from an LLM working with plain JavaScript as opposed to TypeScript, I can't imagine choosing to start a project in Python at the moment.

> I could write in brainfuck with ai, but I presume, wouldn’t get the same results than if going with python.

https://esolang-bench.vercel.app/

Also, every single interpreter error has an entire corpus of StackOverflow-esque fix suggestions alongside it, and the model has been fine-tuned to minimize such errors on the first try. This hasn't been done for more obscure languages. You'll likely take more turns, on average, to get a working output, even if your problem is fully verifiable via test input/outputs - and if it's not verifiable, you don't want the "attention" of the model focused on syntax rather than the solution.

I loved from writing all my code with LLMs from Python to Rust. I’ve seen absolutely no difference, most of the time I couldn’t even tell you which it’s writing in.

My programs are faster and more reliable than they’ve ever been.

I wouldn't say I get worse results with Go than I do with Python.

I wrote about the meta thesis of programming languages in the training data here

https://jry.io/writing/use-boring-languages-with-llms/

that's right, we dont need to care about a lang, same as we dont care about Map when FSD promise its already end to end optimal one.

The LLMs are actually worse at generating Python than other langs, hypothesized due to quality of training data lol.

I still read the generated code, so I'm not quite willing to give up on Python yet though.

There's enough training data on the other langs.

> Read the first few comments and surprised I didn’t see it, but training data. The voluminous amount of Python in the training data.

That's actually part of the point. Almost no one writes types for Python and has complete type compliance. So all that training data is people just yoloing Python, writing a bunch of poor code in it.

I honestly can't believe any experienced software engineer would decide to build systems in Python these days.

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No if that mattered you'd write everything in html and css. Because that has way more training data.

"I could write in brainfuck with ai"

Well, go on and do the experiment! Perhaps LLMs can right code as well in BF as Python but I don't recommend it because hallucinations are really hard to notice in BF.

If you are going to worry about high level computer languages and AI, you are going to have to start with getting to grips with machine code and assemblers and that. Once you know how say some Python code ends up being processed by your laptop CPU(s), then you will know when BF might be best!