Hacker News Reader

Comment by dingdingdang | Hacker News Reader

Talking about Python "somewhere in the middle" - I had a demo of a simple webview gtk app I wanted to run on vanilla Debian setup last night.. so I did the canonical-thing-of-the-month and used uv to instantiate a venv and pull the dependencies. Then attempted to run the code.. mayhem. Errors indicating that the right things were in place but that the code still couldn't run (?) and finally Python Core Dumped.. OK. This is (in some shape or form) what happens every single time I give Python a fresh go for an idea. Eventually Golang is more verbose (and I don't particularly like the mod.go system either) but once things compile.. they run. They don't attempt running or require xyz OS specific hack.

View on HN

Gtk makes that simple python program way more complex since it'll need more than pure-python dependencies.

It's really a huge pain point in python. Pure python dependencies are amazingly easy to use, but there's a lot of packages that depend on either c extensions that need to be built or have OS dependencies. It's gotten better with wheels and manylinux builds, but you can still shoot your foot off pretty easily.

Python is near the top in languages that have given me trouble in other peoples' production software. Everything can be working fine and then one day the planets fall out of alignment or something and the Python portion of the software breaks and the fix is as clear as mud.

I'm pretty sure the gtk dependencies weren't built by Astral, which, yes, unfortunately means that it won't always just work, as they streamline their Python builds in... unusual ways. A few months ago I had a similar issue running a Tkinter project with uv, then all was well when I used conda instead.

Yeah.. this is exactly the overall reality of the ecosystem isn't it? That being said I do hope uv succeeds in their unification effort, there's nothing worse than relying on a smattering of diff package managers and built streams to get basic stuff working. It's like a messy workshop, it works but there's a implicit cost in terms of the lack of clarity and focus for the user. It's a cost I'm not willingly paying.

It may not be the grand unifier if they aren't willing to compromise. Currently I'd say conda is the "grand unifier", giving users 100% what they ask for artifacts-wise, albeit rather slowly. On the other hand, uv provides things super fast, but those things may break 5% of the time in unusual ways on unusual configs. I have no issue using both for the fullest experience.

I have never experienced something like that. How did you use uv? What errors did you get?

How were the dependencies specified? What kind of files were provided for you to instantiate the venv?

I haven't had the same issue with anaconda. Give it a try.

I've had similar issues with anaconda, once upon a time. I've hit a critical roadblock that ruined my day with every single Python dependency/environment tool except basic venv + requirements.txt, I think. That gets in the way the least but it's also not very helpful, you're stuck with requirements.txt which tends to be error-prone to manage.

Reply on news.ycombinator.com