Looks like a promising linting tool for Python. Feature packed and faster than most other options out there.

Looks like an interesting new building block to publish data visualizations.

Nice walk through for a use of PyO3 to make some Python code much faster. Nice to see how useful py-spy turn out to be in such scenarii as well.

This really looks like a nice library for symbolic maths. Keep in mind it's python based but it goes all the way to generating solutions to the given problem in various languages.

Another impressive feat from the people behind the Cosmopolitan project. A self contained and portable binary which run your Django application on almost any platform? Apparently doable. The versions used for the dependencies are a bit old but that's clearly something which will be solved soon.

perf now available also to Python programs. This definitely can be useful for proper profiling.

A bit of a sarcastic tone but a few good point in there. Also shows interesting alternatives to C++ to squeeze every ounce of performance out of your code whatever the platform it runs on. Of the three options explored I knew only about Numba really.

This is clearly a dangerous pitfall in the asyncio API.

Early days but could become an interesting alternative to Lua for an embedded scripting language in some projects.

This is a big milestone reached for that project. Let's hope it'll drive adoption up.

Yes... python packaging is a mess. I wonder when it'll get properly unified and get a proper single tool and workflow.

This is apparently a somewhat common mistake. Something is apparently not easy enough to handle and error prone.

Python is getting faster but is still far from what you can get with C++ of course. That said, for simulations you likely don't want everything in Python or in C++. Part of the challenge is to split the subsystems properly and use C++ where it matters.

This is indeed a problem in the way Pipenv handle this kind of cases. Makes for bisecting clearly troublesome.

Interesting deep dive on how sets and dicts are implemented in CPython. There are a couple of interesting tricks in there.

OK, the writing is sometimes a bit biased in my opinion (didn't you know Python is superior to any other language?). That being said, this is an interesting resource to get ideas on how the GoF proposed set of design patterns apply in the Python world. I like this kind of "how do things relate" resources.

Interesting examples where the Python type hints are used. This still needs improvements but it's clearly improving.

This is good news, this provide more venues for improving performances in Python modules next to switching to compiled Rust with something like PyO3. There's clearly a case to be more for not having to rewrite when the codebase was already mostly Python.

This has some interesting promises in terms of performance using Python. Looks a bit like a CUDA for Python... to be seen how it fares in practice.

Python is making progress regarding portable binaries and it is welcome.