Looks like an interesting tool for infrastructure automation. It's all Python based which is an interesting departure from yaml files in that space. Could be a nice alternative to Ansible. I might take it out for a spin.
Another example of enforcing conventions using automated checks. This time using Python and Django tricks.
Interesting API for running subprocesses and interact with files.
As usual measure and don't just assume when you want to optimize something. This is an interesting case in Python using Numba.
Interesting library if you got to do a lots of heavy analysis work with strings.
Some reasons why Python and C behave differently on this matter. It's a source of mistakes.
Interesting to see WebGPU bindings for Python.
Looks like a nice tool for quick data exploration straight from the command line.
Interesting little web framework to run on MicroPython. Clearly could make some IoT project more accessible.
Want to better understand the JIT approach introduced in Python 3.13, this is a good little article. This JIT is a first step towards more optimizations.
Interesting trick even though I always cringe at such difference of behavior between runtime and "compile" time.
Overview of the differences in behavior between the two most popular ORMs in the Python ecosystem. This shows interesting different design options for such things.
Very interesting musing about undefined behaviors and language constraints. This is a bit Rust focused for obvious reasons but is also looking at what other languages have been doing.
This is unsurprisingly highly depend on the actual code, not only on the hardware.
Nice illustration on how pattern matching can simplify code and make it easier to write.
The fact that they felt the need to write such a letter is troubling. What's going on in the Python Software Foundation really? Something needs to be fixed it seems.
If you wonder what's happening on the JIT front in CPython land, here is a talk explaining what's coming in 3.13.
Since quite a lot of Python code will be impacted by this, better get ready.
Won't be easy to get rid of the GIL in the Python ecosystem. There are notable differences of behavior between implementations and even versions of the same implementation... Lots of user code will unwillingly depend on a specific set of guarantees.
Nice to see the same optimizations than in a previous article play out in Python. By leveraging Numpy and Numba it goes a long way already.