Comment by mountainriver
5 hours ago
Same, I’m not sure how Python survives this outside of machine learning.
All of our services we were our are significantly faster and more reliable. We used Rust, it wasn’t hard to do
5 hours ago
Same, I’m not sure how Python survives this outside of machine learning.
All of our services we were our are significantly faster and more reliable. We used Rust, it wasn’t hard to do
the funny thing is that everyone, including myself, posited that python would be the winner of the ai coding wars, because of how much training data there is for it. My experience has been the opposite.
I felt the opposite, because Python isn’t a great language. It won because of Google, fast prototyping, and its ML interop (e.g. pandas, numpy), but as a language it’s always been subpar.
Indentation is a horrible decision (there’s a reason no other language went this way), which led to simple concepts like blocks/lambdas having pretty wild constraints (only one line??)
Type decoration has been a welcome addition, but too slowly iterated on and the native implementations (mypy) are horribly slow at any meaningful size.
Concurrency was never good and its GIL+FFI story has boxed it into a long-term pit of sadness.
I’ve used it for years, but I’m happy to see it go. It didn’t win because it was the best language.
> lambdas having pretty wild constraints (only one line??)
I will never understand why people are upset about this.
You HAVE multi-line lambdas. They're called functions.
Yeah, I know you want a function that's only used once to be able to be defined in-line, but tbh I've always found that syntax to be pretty ugly, especially once you're passing two functions to a single call, or have additional parameters AFTER the function (I'm looking at you, setTimeout/setInterval).
> there’s a reason no other language went this way)
Except of course for those that did, Haskell, Fortran for example.
1 reply →
I’m always baffled when language complaints come down to syntax
1 reply →
Lambdas are intentionally kneecapped in python because Guido van Robson doesn't want to make a functional language. (As in "functional programming", not that it doesn't work.)
2 replies →
AI benefits from tools to verify its halucinations. That's much easier in a typed and compiled language. Then have a language that can't be monkey patched at runtime and the confidence increases even more.
If you mean "easy to get something out of it" then yeah, it's great.
Typescript wins in terms of training data IMHO, by which I mean that the training data is large enough that AI does great with TS, and the language is (IMHO) superior to Python in many ways.
I personally now use a mixture of Typescript and Rust for most things, including AI coding. Its been working quite well. (AI doesn't handle Rust as well as TS, in that the code isn't quite idiomatic, but it does ok)
It turns out that volume of training data isn't the most important thing. Elixir beats Kotlin and C#, which beat pretty much everything else. Kotlin is probably the sweet spot for most things.
1 reply →
a lot of the training data is either for python 2 or just generally very low quality
The quality issue doesn't seem unique to Python.
The versioning issue I've seen across libraries that version change in many languages.
I don't tend to hit Python 2 issues using LLMs with it, but I do hit library things (e.g. Pydantic likes to make changes between libraries - or loads of the libraries used a lot by AI companies).
1 reply →
That could be it. I still see LLMs fail a set of static typing challenges that I created a couple years ago as a benchmark. Google models still fail it. I wonder if the lack of typing in a lot of the training data makes python harder to reason about?
The tons of python code would be great training data if there was any consistency across the ecosystem. Yet every project I've touched required me to learn it's unique style. Then I'd imagine they practically poisoned half the training set because python2 is subtly different.
You can test on the device directly, without needing to recompile to try something.