The 10x performance wasn't mentioned in the article at all except the title. I watched the video and he does mention it going from 30s to 3s when switching fro

The 10x performance wasn't mentioned in the article at all except the title. I watched the video and he does mention it going from 30s to 3s when switching from a requirements.txt approach to a uv based approach. No comparison was done against poetry. I am unable to reproduce these results. I just copied his dependencies from the pyproject.toml file into a new poetry project. I ran `poetry install` from within Docker (to avoid using my local cache) `docker run --rm -it -v `pwd`:/work python:3.13 /bin/bash` and it took 3.7s I did the same with an empty repo and a requirements.txt file and it took 8.1s. I also did through `uv` and it took 2.1s. Better performance?, sure. A lot better performence?, I can't say that with the numbers I got. 10x performance?... absolutely not. Also, this isn't a major part of anybody's workflow. Docker builds happen typically on release. Maybe when running tests during CI/CD after the majority of work has been done locally.