I don't care if pip technically can do something. The fact that I explicitly have to mess around with venvs and the stuff is already enough mental overhead that I disregard it.
I'm a python programmer at my job, and I've hated the tooling for years. Uv is the first time I actually like working with python.
The problems you're describing, or seeing solved with uv, don't seem to be about a problem with the design of virtual environments. (Uv still uses them.) They're about not having the paradigm of making a venv transiently, as part of the code invocation; or they're about not having a built-in automation of a common sequence of steps. But you can do that just as well with a couple lines of Bash.
I'm not writing any of this to praise the standard tooling. I'm doing it because the criticisms I see most commonly are inaccurate. In particular, I'm doing it to push back against the idea that a non-Python language is required to make functional Python tooling. There isn't a good conceptual reason for that.
But it commonly comes across that people think it can't be written in Python if it's to have XYZ features, and by and large they're wrong, and I'm trying to point that out. In particular, people commonly seem to think that e.g. Pip needs to be in the same environment to work, and that's just not true. There's a system in place that defaults to copying Pip into every environment so that you can `python -m pip`, but this is wasteful and unnecessary. (Pip is designed to run under the install environment's Python, but this is a hacky implementation detail. It really just needs to know the destination paths and the target Python version.)
It also happens that I care about disk footprint quite a bit more than most people. Maybe because I still remember the computers I grew up with.
I might just blow your mind here:
The thing that actually takes time is installing Pip into the venv. I already have local demonstrations that this installation can be an order of magnitude faster in native Python. But it's also completely unnecessary to do that: I have wrappers for this, of course (and I'm explicitly showing the path to a separate Pip that's already on my path for demonstration purposes).> a killer feature is the ability to inline dependencies in your Python source code, then use: uv tool run <scriptname>
Yes, Uv implements PEP 723 "Inline Script Metadata" (https://peps.python.org/pep-0723/) - originally the idea of Paul Moore from the Pip dev team, whose competing PEP 722 lost out (see https://discuss.python.org/t/_/29905). He's been talking about a feature like this for quite a while, although I can't easily find the older discussion. He seems to consider it out of scope for Pip, but it's also available in Pipx as of version 1.4.2 (https://pipx.pypa.io/stable/CHANGELOG/).
> The first run is a bit slower due to downloads and etc, but the second and subsequent runs are a bunch of internal symlink shuffling.
Part of why Pip is slow at this is because it insists on checking PyPI for newer versions even if it has something cached, and because its internal cache is designed to simulate an Internet connection and go through all the usual metadata parsing etc. instead of just storing the wheels directly. But it's also just slow at actually installing packages when it already has the wheel.
In principle, nothing prevents a Python program from doing caching sensibly and from shuffling symlinks around.