I have a Mac with 4TB of storage but it’s still annoying when every new AI app I try installs its own virtual environment with a fresh copy of Python, PyTorch, other duplicate libraries, and then models on top of that.
And now given everybody now does this I guess the incentive to stop breaking stuff reduces even further.
Might as well have static binaries.
It’s a nice language though.
The main difference is that Python use to make you have to know that the virtualenv existed. Now `uv run` and `poetry run` abstract that away so you don’t have to interact with it if you don’t want to.
I'm just speculating that's it's a self reinforcing pattern - compatibility problems leads to isolated builds, which reduces peoples concern for backwards compatibility, which makes isolated builds ever more important.
Maybe it's fine - a trade off that allows greater velocity of development, it just seems attention to backwards compatibility is becoming a thing of the past.
The original plan was to ship Python. However I found out I can migrate them to CoreML, and now it's a model file + Swift code. I got some massive performance improvements as well.
Of course, this doesn't work at all for non-Mac environments, but it was nice to be able to do it. (Also doesn't solve the duplicate large models problem)
Python heaviness is a more fundamental problem.