Interesting read on how the CPython JIT effort has been saved.

Looks like an interesting ORM which brings advantages of the Django one without all the bagage. It's still young, let's see how it evolves.

Here are the main levers to make Python code faster. Tries also to distinguish the effort level of each approach.

Interesting new tricks with the introspection of Python type annotations.

Of course it's a question of the amount of allocations you need.

Looks surprisingly easy to profile the Django startup. Probably makes sense to profile other parts of your application but this is likely a bit more involved.

Looking at several languages and their reflection features. What's coming with C++26 is really something of another class than anything else. I just have concerned about its readability though.

Nice improvement in Python for waiting the end of a subprocess. Explains nicely the underlying options and available syscall if you need to do the same in your code.

Neat little Python trick for testing exceptions.

OK, this is definitely a very cool hack. It can definitely help to debug locally.

A very comprehensive view of Python memory consumption and the speed of common operations. Some of the numbers are higher than I expected.

The situation about file locking is really complicated in the Unix systems family.

Unsurprisingly, this is mostly not related to the use of Rust. The design choices are what male uv so fast.

This is definitely an exciting new option for Python development.

This is a nice little algorithm and it shows how to approach it in Python while keeping it efficient in term of operations.

Some of this might sound obvious I guess. Still there are interesting lesser known nuggets proposed here.

Neat stories explaining those three important features of Python.

First rough benchmarks for CPython 3.14. It looks like the JIT isn't giving much improvement on the cases explored, the free threading on the other hand is a clear advantage for multithreaded cases. Of course as usual Pypy is still way faster. That said, like anything else: first measure in your own context before taking any decision.

It's out now, this is a big one with quite a few interesting features. Let's see how it's used in practice.

Early days but it looks like an interesting use of the t-strings introduced in Python 3.14.