The speed of uv combined with GitLab CI/CD cache enables near-instantaneous
installation of Python requirements compared to pip. Stop helping entropy rise in your CI/CD.
Here, I’ll show a simple example of using native uv package management in your pipeline.
You can easily adapt the same approach for uv pip or even pip itself.
columnThe column tool is pretty neat.
It allows you to format text in columns (how surprising). Less known fact is that itd
can also show output in JSON format. This is useful when you need quick way to convert
text or even CSV to JSON.
I've learned about it form this amazing YouTube video by Veronica Explains.
Sometimes my practical nature is easily bored by the abstract tasks in LeetCode, HackerRank, and other similar platforms. I've found these challenges to be very entertaining and practical.
I am going to keep a list of them here and update it in case I find more. This will also be a good way to keep track of my progress (and a reminder of unfinished tasks :).
The GNU parallel is a great tool for solving embarrassingly parallel problems. Despite the problem class name parallelization is not embarrassing at all and can be hard to implement. In this post I would like to show some of the simple ETL and ML task parallelization examples using GNU parallel.
Recently I've learned about the git bisect command
and I think it's a very powerful and useful tool to find bugs in your codebase. It allows you
to find the commit that introduced a bug by performing a binary search and even
automating the process of checking out commits and testing them.