> I've left this one to the bonus section because I've never used set operations on Counters and I'm finding it extremely hard to think of a use case for xor specifically. But I do appreciate the devs adding it for completeness.
Python is such a weird language. Lazy imports are a bandaid for AI code base monstrosities with 1000 imports (1% of which are probably Shai Hulud now).
And now even type imports are apparently so slow that you have to disable them if unused during the normal untyped execution.
If Instagram or others wants a professional language, they should switch to Go or PHP instead of shoehorning strange features into a language that wasn't built for their use cases.
I think this is just a natural consequence of an easy-to-use package system. The exact same story as with node. If you don't want lots of imports, don't make it so damn easy to pile them into projects. I'm frankly surprised we still see so few supply chain attacks, even though they picked up their cadence dramatically.
Empirically, I have used the current accepted way to do lazy imports (import statement inside a function) before AI coding was even a mainstream thing, for personal code that sometimes needs a heavy import and sometimes doesn’t.
The lazy statement would be an improvement as it allows one to see all the imports at the top where you expect them to be.
As a now deleted comment pointed out, lazy imports had been requested forever. They were rejected forever and were accepted just when BigCorps wanted them.
Python-dev now is paid to shore up the failed Instagram stack.
I was so into Python for 10 years, was enjoyable to work in. But have deleted 100k+ lines this year already moving them to faster languages in a post AI codebot world. Mostly moving to go these days.
This is straightforward in the first instance, but how do you see maintenance of those projects going forward - especially adding more complex features ?
I can see one way forward being to prototype them in python and convert.
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.
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.
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 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).
Check out symmetric difference
https://en.wikipedia.org/wiki/Symmetric_difference
And now even type imports are apparently so slow that you have to disable them if unused during the normal untyped execution.
If Instagram or others wants a professional language, they should switch to Go or PHP instead of shoehorning strange features into a language that wasn't built for their use cases.
Just because you don’t like a feature doesn’t mean it’s because of AI and bad code.
The lazy statement would be an improvement as it allows one to see all the imports at the top where you expect them to be.
Python-dev now is paid to shore up the failed Instagram stack.
I can see one way forward being to prototype them in python and convert.
Try and write a signal processing thing with filters, windowing, overlap, etc. - there's no easy way to do it at all with the libraries that exist.
All of our services we were our are significantly faster and more reliable. We used Rust, it wasn’t hard to do
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.
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).