You might be interested in pixi, which is roughly to conda as uv is to pip (also written in Rust, it reuses the uv solver for PyPI packages)
It's still very immature but if you have a mixture of languages (C, C++, Python, Rust, etc.) I highly recommend checking it out.
It makes building FreeCAD pretty trivial, which is a huge deal considering FreeCAD’s really complex Python and non-python, cross-platform dependencies.
On the python front, however, I am somehow still an old faithful - poetry works just fine as far as I was every concerned. I do trust the collective wisdom that uv is great, but I just never found a good reason to try it.
Perl was the language of the big hack. It had very little to offer in the way of abstractions and OO. So you were kind of excused from writing well structured code and people wrote a lot of janky, untestable stuff in perl and carried on doing than in python.
In python, to get good code you absolutely have to have unit tests and some kind of integration tests. I worked on a roughly 50,000 line python program. It wasn't bad even though we didn't have type hints then. Occasionally we did discover embarassing bugs that a statically typed language would not have permitted but we wrote a test and closed that door.
But day to day, I am happy to write Kotlin and Dart :D they give you amazing tooling and just good enough type guarantees.
Anyway, good to have a bunch of options :).
1. Installation & dependencies: Don't install Python directly, instead install pyenv, use pyenv to install python and pip, use pip to install venv, then use venv to install python dependencies. For any non-trivial project you have to be incredibly careful with dependency management, because breaking changes are extremely common.
2. Useless error messages: I cannot remember outside of trivial examples without external packages when the error message I got was actually directly pointing towards the issue in the code. To give a quick example (pointing back to the point above), I got the error message "ImportError: cannot import name 'ChatResponse' from 'cohere.types'". A quick google search reveals that this happens if a) the cohere API-Key isn't set in ENV or b) you use langchain-cohere 0.4.4 with cohere 5.x, since the two aren't compatible.
3. Undisciplined I/O in libraries: Another ML library I recently deployed has a log-to-file mode. Fair enough, should be disabled before k8s deployment no biggie. Well, the library still crashes because it checks if it has rwx-permissions on a dir it doesn't need.
4. Type conversions in C-interop: Admittedly I was also on the edge of my own capabilities when I dealt with these issues, but we had issues with large integers breaking when using numpy/pandas in between to do some transforms. It was a pain to fix, because Python makes it difficult to understand what's in a variable, and what happens when it leaves Python.
1. and 4. are mainly issues with people doing stuff in Python it wasn't really designed to do. Using Python as a scripting language or a thin (!) abstraction layer over C is where it really shines. 2. and 3. have more to do with the community, but it is compounded by bad language design.
2. I've used a ton of languages and frankly Python has the best tracebacks hands-down, it's not even close. It's not Python's fault a 3rd party library is throwing the wrong error.
3. Again, why is bad language design a library can do janky things with I/O?
4. FFI is tricky in general, but this sounds like primarily a "read the docs" problem. All of the major numeric acceleration libraries have fixed sized numbers, python itself uses a kind of bigint that can be any size. You have to stay in the arrays/tensors to get predictable behavior. This is literally python being "a thin abstraction layer over C."
2. I would argue that the ubiquity of needing stack traces in Python is the main problem. Why are errors propagating down so deep? In Rust I know I'm in trouble when I am looking at the stack trace. The language forces you to handle your errors, and while that can feel limiting, it makes writing correct and maintainable code much more likely. Python lets you be super optimistic about the assumptions of your context and data - which is fine for prototyping, but terrible for production.
3. I agree that this isn't directly a language design issue, but there's a reason I feel the pain in Python and not in Rust or Java. Dynamic typing means you don't know what side effects a library might have until runtime. But fundamentally it is a skill issue. When I deploy the code of Java, Go or Rust people, they generally know I/O is important, and spent the necessary time thinking about it. JS, Python or Ruby devs don't.
4. The issue is that Python's integer handling sets an expectation that numbers "just work," and then that expectation breaks at the FFI boundary. And once you're of the trodden path, things get really hard. The cognitive load of tracking which numeric type you're in at any moments sucks. I completely agree that this was a skill issue on my part, but I am quite sure that I would not have had that problem if it was in a properly type-set, compiled language.
Static typing (in most industrially popular languages) doesn't tell you anything about side effects, only expected inputs and return values.
Dynamic languages let you do a lot of things with meta classes and monkey-patching that allow you to use a library without needing to build your own special version of it. With some C or C++ library that did something bad (like the logging one you mentioned) there's nothing for it but to rebuild it. With Python you may well be able to monkey patch it and live to fight another day.
It is great when you're dealing with 3rd party things that you either don't have the source code for or where you cannot get them to accept a patch.
Now of course, we should have long removed the monkey-patch, or properly documented it somewhere, or I should have been more knowledgeable about Ruby and known that monkey-patching was a thing, but there's a reason why it is a pretty frowned upon practice.
I don’t understand why anybody would ever develop anything in Python other than “I want to write software but can’t be arsed to follow software design principles”, with all the mess that follows from it.
This is such a deeply unserious take. Do you have hundreds of thousands of hours to give out for free? No?