Until last year, every time I started an AI project I'd go through the same ritual. python -m venv .venv , source .venv/bin/activate , pip install anthropic openai ... and then wait. Sometimes over two minutes. Watching anthropic, torch, and pydantic download one by one. The context-switching cost added up. Every time I made a new experiment branch, environment setup broke the flow. The "works on my machine" problem didn't go away either. Then I started using uv — a Python package manager written in Rust, from the team behind Ruff at Astral. I benchmarked it today while setting up a Claude SDK project: installing anthropic along with 16 packages total took 0.874 seconds . With pip, that same operation would have taken 20〜40 seconds. This is a complete hands-on guide to setting up an AI development environment with uv 0.11 from scratch. Why uv Now — What's Actually Wrong with pip, Poetry, and conda Honestly, pip itself isn't the problem. It's a proven tool that's downloaded billions of packages.…