lazy from typing import Iterator
def stream_events(...) -> Iterator[str]:
while True:
yield blocking_get_event(...)
events = stream_events(...)
for event in events:
consume(event)
Oh, that is such a nice thing.
The example:
>> 'hello'.toUpperCase()
Traceback (most recent call last):
...
AttributeError: 'str' object has no attribute 'toUpperCase'. Did you mean '.upper'?https://peps.python.org/pep-0649/
https://docs.python.org/3/reference/compound_stmts.html#anno...
With 3.15, using lazy typing imports is more or less an alternative to putting such imports behind an "if TYPE_CHECKING" guard.
> from __future__ import annotations (PEP 563) will continue to exist with its current behavior at least until Python 3.13 reaches its end-of-life. Subsequently, it will be deprecated and eventually removed.
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.
Counter-example is Go and init() function.
Most languages have this feature Afaik
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.
If you’re asking whether project leads give more weight to a single, tangible, vocal stakeholder than they do to unknown numbers of anonymous and lightly-engaged stakeholders? Yes.
Check out symmetric difference
If they only considered parities it could be interpreted as addition in F_2, which is more natural, but I'd still agree that it's hard to see how you'd use something like this in practice.
1. The very first steps are quite simple. Hello world is literally just `print("hello world")`. In other languages it can be a lot more complex.
2. It got a reputation as a beginner-friendly language as a result.
3. It has a "REPL" which means you can type code into a prompt and it will execute it interactively. This is very helpful for research (think AI) where you're trying stuff out and want to plot graphs and so on.
IMO it is undeservedly popular, or at least was. Wind back 10 years to when it was rapidly gaining mindshare:
1. While "hello world" is simple, if you went further to more complex programs you would hit two roadblocks: a) the lack of static type checking means large programs are difficult to maintain, and b) it's really really slow.
2. While the language is reasonable, the tooling (how you install packages, manage the code and so on) was eye-bleedingly abysmal.
3. While the REPL did technically exist, it was really bare bones. It couldn't even handle things like pasting code into it if the code contained blank lines (which it usually does).
However since it has become arguably the most popular language in the world, a lot of people have been forced to use it and so it is actually getting quite decent now. It has decent static types (even if lots of people still don't use them), the REPL is actually decent now (this changed very recently), and there's a new third party tool called `uv` to manage your code that is actually good.
The biggest issue with it now is that it's still horrifically slow (around 50-200x slower than "fast" languages like C++, Rust etc). It is pretty unlikely that that will ever change. People always try to excuse this by saying Python is a "glue" language and you just use it to connect components written in faster languages, but a) that's pure "you're holding it wrong", and b) that only works in some cases where there are nicely separated "slow bits" that can be moved to another language. That's the case for AI for example, where it's all numerical, but for lots of things it isn't. Mercurial was a competitor to Git that was written in Python and lost partly because it was way too slow. They've started writing parts in Rust but it took them 10 years to even start doing that and by then it was far too late.
> what would you suggest?
It really depends on what you want to make. I would pick something to make first and then pick the language based on that. Something like:
* AI: Python for sure. Make sure you use uv and Pyright.
* Web-based games: Typescript
* Web sites: Typescript, or maybe Go.
* Desktop GUI: Tbh I'd still use C++ with QtWidgets. Getting a bit old-school now tbf.
Also Rust is the best language of them all, but I dunno if I'd pick it as a beginner unless you really know you want to get into programming.
Choosing a language is a game of trade-offs: potentially slower execution in return for faster development time, for example. If your team is already familiar with Ruby, will asking them to write a project in Rust necessarily result in a better product? Maybe, but it will almost certainly take much longer.
Anyway, how many Python programs are actually "too slow"? Most of the time, Python is fast enough, even if heavy computation is offloaded to other languages.
As for Rust being the best language of them all, that's, like, your opinion, man.
Yes it strains at the big to huge project end, not recommended to take it there. Still there are better tools to help now.
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.
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).
Except of course for those that did, Haskell, Fortran for example.
It’s pretty ok in Python, but meaningful indentation is amazing with a proper type system and compiler. Clean, consistent, efficient, and ensures working code is easily read and standardized.
I’m unaware of anyone accepting improperly formatted C# as ‘done’, and would reject any such PR out of hand because of the potential for legibility issues to hide bugs. So: if it were done when 'tis done, then 'twere well it were done by the compiler to save line noise.
"I don't think it makes much sense to try to add "functional" primitives to Python, because the reason those primitives work well in functional languages don't apply to Python, and they make the code pretty unreadable for people who aren't used to functional languages (which means most programmers). I also don't think that the current crop of functional languages is ready for mainstream." https://developers.slashdot.org/story/13/08/25/2115204/inter...
If you mean "easy to get something out of it" then yeah, it's great.
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)
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).
I also don’t have issues with quality of Python generated. It takes a bit of nudging to use list comps and generators rather than imperative forms but it tends to mimic code already in context. So if the codebase is ok, it does do better.
- You wrote 100K lines of code (I've worked on several large C++ projects that were far smaller)
- You wrote those lines in Python (surely the whole point of Python is to write less code)
- You deleted them (never delete anything, isn't this what modern VCS is all about?)
But whatever floats your boat.
The person said: "deleted 100k+ lines this year already moving them to faster languages"
Are you saying that when you move code to another language/rewrite in another language, you leave the original languages code in your repo?
They didn't say they deleted it from their git history. I delete code all the time (doesn't mean its "gone", just that its not in my git head).
Deleting "from my codebase" doesn't imply deleting it from history or backups. Just that the code isn't present for future edits or deployments.
The way you're talking, it sounds like you never delete code from your codebase. Do you just comment it out when you change a line to something else or replace a function with a new one? Just add new files?
Our entire business runs on 300k lines of Ruby (on Rails) and I can keep most of the business logic in my head. I would say our codebase is not exactly “tiny” and just cracking the ceiling into “smal” territory. And comparatively, people probably write even less code in equivalent rails apps to django ones. 100k lines of C++ is miniscule.
Obviously “deleting code” in this context doesn’t mean purging version control history but the current state of the codebase.
No, no, it is not, or at least not in my experience (I do not and never have done web development - medium performance C++ code - I don't see how I could write, understand and support 100K lines of code in this area).
And so, what does your Ruby code actually do?
Our experiences differ then. Mine is that almost all of the code I write is directly targeted on the usually quite complex problem I am trying to solve. I don't do boilerplate, for example.
I would need some evidence of that.
lazy from typing import Iterator
def stream_events(...) -> Iterator[str]: while True: yield blocking_get_event(...)
events = stream_events(...)
for event in events: consume(event)
But damn, with all the supply chain attacks now in the news, could they just make a simple way (for non python insiders) to install python apps without fearing to be infected by a vermin with full access to my $HOME ...
The very idea that you offer a (python) package installer that is gonna pull a tree of code published and updated by random people in an unvetted manner open the door to all the supply chain attacks we are seeing.
Around the same time (early 90s) Java was designed with high isolation in mind but the goal and vision was very different. And Java had its own problems.
I'm saying that because at some point the security problem is gonna really hurt the python ecosystem.