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Comment by dirkc | Hacker News Reader

I suspect that my normal workflows might just have evolved to route around the pain that package management can be in python (or any other ecosystem really).

In what situations are uv most useful? Is it once you install machine learning packages and it pulls in more native stuff - ie is it more popular in some circles? Is there a killer feature that I'm missing?

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If you have hundreds of different Python projects on your machine (as I do) the speed and developer experience improvements of uv make a big difference.

I love being able to cd into any folder and run "uv run pytest" without even having to think about virtual environments or package versions.

Do you run those projects on the host system as your normal user without any isolation?

Yes, which makes me very vulnerable to supply chain attacks.

Yikes! I had a scare once, and since then I only run sandboxed code or scripts I've written with minimal 3rd party deps.

I assume you have other mitigations in place?

Not really. I have good backups and I try to stick with dependencies I trust.

I do a lot of my development work using Claude Code for web which means stuff runs in containers on Anthropic's servers, but I run things on my laptop most days as well.

Imo, uv scripts with the dependencies in the header.

https://docs.astral.sh/uv/guides/scripts/#declaring-script-d...

I guess that could be useful. I don't have many standalone python scripts, and those that I do have are very basic. It would be really nice if that header could include sandboxing!

So much this! I've been bugging Astral about addressing the sandboxing challenge for a while, I wonder if that might take more priority now they're at OpenAI?

UV is most useful because it is so much faster than everything else. All the other features I could do without.

Yep, the speed is nice, I can't argue with that!

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