Do people use python for new projects apart from ML stuff which hasn't moved to all-native yet?
My experience with Python is a really bad one for professional work: it's chaotic and slow, and has by far the worst versioning and packaging story of any mainstream language, yet its proponents keep praising it in denial.
I guess Python is an ok target for agentic coding, but my god do look Claude's commit messages pretentious, with code bases quickly heading into absolute unmaintainability. At least it had found gross JS injection vectors in a Django app that really shouldn't have made it through a code review, architecture level as they were, but oh well. A mature Django app is also not a nice dev experience IMO, with tons of implicit behavior all over the place encoded in a mix of magic filenames, database naming conventions, and URL routing quickly descending into regexp hacks.
> My experience with Python is a really bad one for professional work: it's chaotic and slow, and has by far the worst versioning and packaging story of any mainstream language, yet its proponents keep praising it in denial.
Some people just don't have the experience you do, "its proponent keep praising it in denial", can we have a better level of debate, come on now.
The slowest of all dynamic scripting languages. Breaking ABI's and API's left and right all the time. Not able to implement basic performance optims. Their infrastructure (pip) getting worse and worse, getting everyone to install private venv's for every app, leading to missing security updates, because updates just break everything.
I don't think it's chaotic. I won't deny it's somewhat slow however usually anything performance sensitive gets shoved in a native code extension anyway.
As for packaging, I haven't had any problems with poetry or uv. The only time I ever had issues was with Windows in corporate environments where wheels were unavailable and it was also basically impossible to get the right toolchain installed for native code. However, not being able to install a compiler is not really a Python problem
Claude Code has a setting to change the git template so it doesn't attribute itself or you can also commit manually.
Half or more of the scientific research community live and breathe Python. Granted, it's Python 3.12, as 3.13 broke most of the C API, and everything COBOL and Fortran just about ground to a halt. But new projects are spun up constantly.
I guess these kinds of priorities are exactly why Python is not my favorite programming language and why you have tens of Python versions installed on any machine. Not to talk about the Python 2 -> 3 drama that was also about fetishising syntax and pureness over pragmatism, installed base, and respect for existing code.
I suspect 3.14.4 could have been tweaked slightly to address the issue without a revert - they could have prioritized checking the liveliness of objects sorted by size. I’m pretty sure that would fix the max RSS issue without needing a revert and the people unhappy with 3.14 could keep using 3.13 or switch to 3.14 and simply inject explicit calls to gc.gc().
Figuring out how to measure the size of an object can be tricky of course, but I suspect there’s all sorts of things you could try including figuring out how much memory got deallocated after you gc a cycle and attributing it to where the object got allocated as a heuristic to measure the mean allocation size.
> I suspect 3.14.4 could have been tweaked slightly to address the issue without a revert
I'm sure all the people that have been working on this for years would be interested in your small tweak, that they didn't think of, and would happily accept the PR!
a) do work to reduce issues as they come up
b) appease the vocal complaints
A takes work, guts, and risk. Option b was chosen with the GC work basically saddled with so much process it’s never going to change. Python has a very storied history of being very committee driven design so the committee did the committee thing.
The usual GC tradeoff is between memory and CPU performance.
If you set the memory max high, the GC will run less often, you get less pause time.
So I do not understand why it's a surprise that minimizing the pause time requires more memory. Is it because there is no knob to set either the max pause time or the max memory ?
We definitely noticed behavioral differences in 3.14 regarding gc which could show up in particular test suites we have that are purposely ensuring all objects of a certain type were collected after a gc.collect() run. Between this and other issues (changes to the runtime API for typing, the first decently runnable version of free-threading, kind of a longer time for some C-based dependencies to catch up), the transition for my projects (SQLAlchemy) to 3.14 was generally more bumpy than that of say 3.12 or 3.13. will be interesting to see if 3.14.5 allows us to relax some changes we had to make to the test suite.
> Python 3.14.0 introduced a new incremental garbage collector. But reports of higher memory usage caused the Python team to revert the garbage collector changes in 3.14.5.
If they didn't have very good objective reasons the new GC is better, they never should have shipped it. If they do, they should not have reverted the change.
It's better in some ways (order-of-magnitude reductions in pause times were cited) but worse in other ways (higher peak memory usage). That the higher peak memory usage was catastrophic for some users only became apparent through post-release feedback.
They should have shipped it as an addon GC, not enabled by default. One could have turned it on with a command line switch or an env var, just like the Ruby JIT.
It's this sort of stuff that leaves me scratching my head why people like Python so much. I hear them say they prefer the syntax and personally I feel like that's such a small part of the holistic experience of working with any particular language. It's one of the reasons why I gave up on C++ years ago for .NET, the whole system of tooling in .NET has never left me feeling like I was pigeonholed into doing things in stupid, self-flagelating ways. Why should I use a language like C++ that doesn't provide a standard set of package management and build tools? Why should I use a language like Python that feels like it's being designed by amateurs?
I felt like the tooling in Racket, CLisp, and Java were similarly pragmatic and not either religiously devoted to some concept of "backwards compatibility" that I seriously doubt most people actually need, or "ease of use" that actually proves itself to be easy when you consider the not-happy-path of the beginner tutorials. Racket, I didn't continue just because the library ecosystem isn't mature enough to keep up with the latest in databases and other 3rd party services. Java I quit largely because of Oracle and some 2010s problems with stagnation. CLisp mostly because it was too hard to socialize. But never because I thought the core language and tooling were holding me back.
You are right the syntax is a small part of it, but it is more important than you say in this case because Python syntax is one of the things that makes it very readable.
Even if you dislike the direction Python is going in, a lot of what attracted people to Python in the first place is still there. The readability, the large standard library, the huge ecosystem. There are libraries and frameworks for everything: numerical stuff, web development, GUIs etc. Its actually a nice language in itself, just going in the wrong direction now.
If you look at it historically it was really good comparatively. If you compare it to the alternatives available 20 years ago it looks pretty good.
It's easy to start learning on, or prototype with, and then sometimes momentum just keeps it going. Also it may not really be the best at anything, but it's "pretty good" at just about everything. It's kind of like vanilla ice cream.
Packaging can be irritating although uv takes the sting out a bit.
You are right that outside of verbosity, once you get used to the syntax of a language, the value of one over the other kind of fades.
Syntax really does matter more than you give it credit for. Were that not the case, I'd expect one Lisp or Scheme dialect or other to take Python's place. Outside of that counterfactual, Python's competition was stuff like Ruby, and it turned out that network effects were also pretty important.
Python is mostly about the “batteries included” standard library and what’s becoming nearly standard third party libs, being able to play around in the REPL,
> It's this sort of stuff that leaves me scratching my head why people like Python so much
Because of the libraries, not necessarily the language, which is also quite straightforward. For example we found a niche library that speaks the ISO-TP protocol in Python, which allows us to communicate with vehicle ECUs. That's why people also use C++, even tough I quite doubt it's because they like the language. Add to that that it's also heavily used in embedded programming. Yes, you could call a C/C++ library from another language, depending how well the language can do that.
I prefer Ruby, but Python probably has just about everything one would need. It's also great for data processing. We hardly have anything better than pandas, polars, numpy, scipy in other languages and that:s without even mentioning ML tooling.
Do people use python for new projects apart from ML stuff which hasn't moved to all-native yet?
My experience with Python is a really bad one for professional work: it's chaotic and slow, and has by far the worst versioning and packaging story of any mainstream language, yet its proponents keep praising it in denial.
I guess Python is an ok target for agentic coding, but my god do look Claude's commit messages pretentious, with code bases quickly heading into absolute unmaintainability. At least it had found gross JS injection vectors in a Django app that really shouldn't have made it through a code review, architecture level as they were, but oh well. A mature Django app is also not a nice dev experience IMO, with tons of implicit behavior all over the place encoded in a mix of magic filenames, database naming conventions, and URL routing quickly descending into regexp hacks.
> My experience with Python is a really bad one for professional work: it's chaotic and slow, and has by far the worst versioning and packaging story of any mainstream language, yet its proponents keep praising it in denial.
Some people just don't have the experience you do, "its proponent keep praising it in denial", can we have a better level of debate, come on now.
It's the classic worse is better.
The slowest of all dynamic scripting languages. Breaking ABI's and API's left and right all the time. Not able to implement basic performance optims. Their infrastructure (pip) getting worse and worse, getting everyone to install private venv's for every app, leading to missing security updates, because updates just break everything.
People just love trouble.
I'm split on Django--it's ok but I don't love it.
I don't think it's chaotic. I won't deny it's somewhat slow however usually anything performance sensitive gets shoved in a native code extension anyway.
As for packaging, I haven't had any problems with poetry or uv. The only time I ever had issues was with Windows in corporate environments where wheels were unavailable and it was also basically impossible to get the right toolchain installed for native code. However, not being able to install a compiler is not really a Python problem
Claude Code has a setting to change the git template so it doesn't attribute itself or you can also commit manually.
Half or more of the scientific research community live and breathe Python. Granted, it's Python 3.12, as 3.13 broke most of the C API, and everything COBOL and Fortran just about ground to a halt. But new projects are spun up constantly.
3.13 broke most of the C API
3.14 broke GC
I guess these kinds of priorities are exactly why Python is not my favorite programming language and why you have tens of Python versions installed on any machine. Not to talk about the Python 2 -> 3 drama that was also about fetishising syntax and pureness over pragmatism, installed base, and respect for existing code.
Python is the new Matlab and Mathematica combined.
2 replies →
> apart from ML stuff
More and more applications need to use ML these days. So Python use will only grow.
Related. 31 days ago
Reverting the incremental GC in Python 3.14 and 3.15
265 points, 130 comments
https://news.ycombinator.com/item?id=48077924
I suspect 3.14.4 could have been tweaked slightly to address the issue without a revert - they could have prioritized checking the liveliness of objects sorted by size. I’m pretty sure that would fix the max RSS issue without needing a revert and the people unhappy with 3.14 could keep using 3.13 or switch to 3.14 and simply inject explicit calls to gc.gc().
Figuring out how to measure the size of an object can be tricky of course, but I suspect there’s all sorts of things you could try including figuring out how much memory got deallocated after you gc a cycle and attributing it to where the object got allocated as a heuristic to measure the mean allocation size.
> I suspect 3.14.4 could have been tweaked slightly to address the issue without a revert
I'm sure all the people that have been working on this for years would be interested in your small tweak, that they didn't think of, and would happily accept the PR!
The options are
a) do work to reduce issues as they come up b) appease the vocal complaints
A takes work, guts, and risk. Option b was chosen with the GC work basically saddled with so much process it’s never going to change. Python has a very storied history of being very committee driven design so the committee did the committee thing.
3 replies →
The usual GC tradeoff is between memory and CPU performance. If you set the memory max high, the GC will run less often, you get less pause time.
So I do not understand why it's a surprise that minimizing the pause time requires more memory. Is it because there is no knob to set either the max pause time or the max memory ?
Instead of "Reference counting primer" I would like to see a primer on how exactly such a huge change can go into Python without formal PIP process.
Anthony Sottile had a video on the reversion over a month ago: https://www.youtube.com/watch?v=tQ3hnQiJ0YM
We definitely noticed behavioral differences in 3.14 regarding gc which could show up in particular test suites we have that are purposely ensuring all objects of a certain type were collected after a gc.collect() run. Between this and other issues (changes to the runtime API for typing, the first decently runnable version of free-threading, kind of a longer time for some C-based dependencies to catch up), the transition for my projects (SQLAlchemy) to 3.14 was generally more bumpy than that of say 3.12 or 3.13. will be interesting to see if 3.14.5 allows us to relax some changes we had to make to the test suite.
There was an issue recently with synapse, tbe matrix server implementation, where ram usage would grow until OOM.
The solution was to upgrade Python. But I won't, because that was the problem in the first place, here, apparently.
Oddly if I ran the whole thing under memray with a different allocator, no issue. I say oddly but it isn't.
So I guess my matrix server is broken until I rehome it on a new server with a fresh python instead of 3.10.8.
Just run it under uv :)
but I aways use Python to analysis my medical data.
The folks running the show as it relates to python remind me a lot about the folks running the show at mozilla.
That is not a compliment.
[flagged]
> Python 3.14.0 introduced a new incremental garbage collector. But reports of higher memory usage caused the Python team to revert the garbage collector changes in 3.14.5.
If they didn't have very good objective reasons the new GC is better, they never should have shipped it. If they do, they should not have reverted the change.
It's better in some ways (order-of-magnitude reductions in pause times were cited) but worse in other ways (higher peak memory usage). That the higher peak memory usage was catastrophic for some users only became apparent through post-release feedback.
They should have shipped it as an addon GC, not enabled by default. One could have turned it on with a command line switch or an env var, just like the Ruby JIT.
Really? You've never reverted a positive change because it contained a regression only discovered after release?
It's this sort of stuff that leaves me scratching my head why people like Python so much. I hear them say they prefer the syntax and personally I feel like that's such a small part of the holistic experience of working with any particular language. It's one of the reasons why I gave up on C++ years ago for .NET, the whole system of tooling in .NET has never left me feeling like I was pigeonholed into doing things in stupid, self-flagelating ways. Why should I use a language like C++ that doesn't provide a standard set of package management and build tools? Why should I use a language like Python that feels like it's being designed by amateurs?
I felt like the tooling in Racket, CLisp, and Java were similarly pragmatic and not either religiously devoted to some concept of "backwards compatibility" that I seriously doubt most people actually need, or "ease of use" that actually proves itself to be easy when you consider the not-happy-path of the beginner tutorials. Racket, I didn't continue just because the library ecosystem isn't mature enough to keep up with the latest in databases and other 3rd party services. Java I quit largely because of Oracle and some 2010s problems with stagnation. CLisp mostly because it was too hard to socialize. But never because I thought the core language and tooling were holding me back.
You are right the syntax is a small part of it, but it is more important than you say in this case because Python syntax is one of the things that makes it very readable.
Even if you dislike the direction Python is going in, a lot of what attracted people to Python in the first place is still there. The readability, the large standard library, the huge ecosystem. There are libraries and frameworks for everything: numerical stuff, web development, GUIs etc. Its actually a nice language in itself, just going in the wrong direction now.
If you look at it historically it was really good comparatively. If you compare it to the alternatives available 20 years ago it looks pretty good.
C++ has vcpkg, conan, cmake and ninja nowadays.
They are as standard as arguing about Ant, Maven, Gradle in Java, npm, pnpm, yarn in node, and so on.
However I fully agree with the gist of your comment, basically Python is the new BASIC.
However at least BASIC was compiled, with exception of the 8 bit home micros.
It's easy to start learning on, or prototype with, and then sometimes momentum just keeps it going. Also it may not really be the best at anything, but it's "pretty good" at just about everything. It's kind of like vanilla ice cream.
Packaging can be irritating although uv takes the sting out a bit.
You are right that outside of verbosity, once you get used to the syntax of a language, the value of one over the other kind of fades.
4 replies →
> Racket, CLisp
Syntax really does matter more than you give it credit for. Were that not the case, I'd expect one Lisp or Scheme dialect or other to take Python's place. Outside of that counterfactual, Python's competition was stuff like Ruby, and it turned out that network effects were also pretty important.
Python is mostly about the “batteries included” standard library and what’s becoming nearly standard third party libs, being able to play around in the REPL,
6 replies →
> It's this sort of stuff that leaves me scratching my head why people like Python so much
Because of the libraries, not necessarily the language, which is also quite straightforward. For example we found a niche library that speaks the ISO-TP protocol in Python, which allows us to communicate with vehicle ECUs. That's why people also use C++, even tough I quite doubt it's because they like the language. Add to that that it's also heavily used in embedded programming. Yes, you could call a C/C++ library from another language, depending how well the language can do that.
I prefer Ruby, but Python probably has just about everything one would need. It's also great for data processing. We hardly have anything better than pandas, polars, numpy, scipy in other languages and that:s without even mentioning ML tooling.