But that's not how the argument is used in practice. In practice this argument is used to justify bloated apps, bad engineering, and corner-cutting. When people say “users don’t care about your tech stack,” what they really mean is that product quality doesn’t matter.
Yesterday File Pilot (no affiliation) hit the HN frontpage. File Pilot is written from scratch and it has a ton of functionality packed in a 1.8mb download. As somebody on Twitter pointed out, a debug build of "hello world!" in Rust clocks in at 3.7mb. (No shade on Rust)
Users don't care what language or libraries you use. Users care only about functionality, right? But guess what? These two things are not independent. If you want to make something that starts instantly you can't use electron or java. You can't use bloated libraries. Because users do notice. All else equal users will absolutely choose the zippiest products.
But most of my work, since I adopted conda 7ish years ago, involves using the same ML environment across any number of folders or even throw-away notebooks on the desktop, for instance. I’ll create the environment and sometimes add new packages, but rarely update it, unless I feel like a spring cleaning. And I like knowing that I have the same environment across all my machines, so I don’t have to think about if I’m running the same script or notebook on a different machine today.
The idea of a new environment for each of my related “projects” just doesn’t make sense to me. But, I’m open to learning a new workflow.
Addition: I don’t run other’s code, like pretrained models built with specific package requirements.
My one off notebook I'm going to set up to be similar to the scripts, will require some mods.
It does take up a lot more space, it is quite a bit faster.
However, you could use the workspace concept for this I believe, and have the dependencies for all the projects described in one root folder and then all sub-folders will use the environment.
But I mean, our use case is very different than yours, its not necessary to use uv.
it does virtualenv, it does pyenv, it does pip, so all thats managed in once place.
its much faster than pip.
its like 80% of my workflow now.