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.

Let's put this quote back in its context, shall we?

One of the best developer tools around for analysis and profiling. I'm glad it exists, saved me a few times.

Wow, this is a very good exploration of the performances of several common languages and runtimes. This is one of the most thorough I've seen. A good resource for deciding what to pick.

And this is why you likely need to optimize your data pipelines at some point. There are plenty of levers available.

Excellent deepdive about pipes, on the path to optimization we see how perf is used, how memory is managed by the kernel etc. Very thorough.

Debatable "feature", bad implementation, dubious community handling... Clearly not a good example to follow from the Go space.

This looks like a very interesting tracing tool for debugging and profiling purposes.

That looks like a very interesting tool for larger Python based projects. Definitely need a way to profile memory use in there.

Oh this is really neat! This is a good way to visualize how it evolved over time, I find the period starting in 2005 especially interesting.

Really cool optimizations for B-Trees. Once the layout is reworked this is a neat way to use SIMD as well.

Interesting use of WebAssembly for fast and very portable code. Also especially interesting is the care in the move to the new software architecture.

Interesting tips for potential bottlenecks in your queries.

Not necessarily unknown paths to squeeze more performance out of Python. Still it's nice to have those options measured and listed in the same post.

Good reminder that CORS can have an impact regarding the performance of your application.

Good reminder on how a shared atomic can become a huge bottleneck in multi-CPU setups.

Mostly about the general approach on how to profile this kind of things. Still a couple of interesting pytest specific tips in here.

Interesting exploration and workaround for the Postgres query planner.

This looks like an interesting full system profiler.