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A recap on the work done in NumPy and CPython to make multi-threaded NumPy workloads scale on the free-threaded build of CPython.
Floating point math is often slower than integer math because the compiler is being conservative about how it optimizes your code. While some programming languages already had solutions of a sort, until now Rust did not have a good stable way to deal with this limitation. But now, starting in version 1.98, Rust will allow telling the compiler it can optimize your code further—but with extra control so that you can still write numeric algorithms with minimal rounding errors.
Mark writes articles on data analysis. This one is all about the images NASA released from the Artemis II mission. It includes step-by-step instructions that you can follow along.
A practical Git walkthrough for data scientists, focused on real workflows like branching for experiments, reverting mistakes, and keeping project history clean with small, focused commits. It also explains merge vs. rebase, why you should not rebase shared branches, and how to set up .gitignore for data-heavy projects.
An introduction to the concept of the Application Binary Interface (ABI), the various CPython ABIs, and the new abi3t stable ABI in Python 3.15.
`lzma` compresses the most and `zlib` is about as fast as `zstd` in `django_redis` as compressor.
Ken is one of the key contributors to the experimental JIT. This post talks about how Python 3.15’s interpreter profiling mode is helping them figure out what is working with the JIT and what isn’t.
Cheatsheet with visual diagrams that explain how the iterables from itertools work. This cheatsheet contains diagrams that explain how the iterables from the module itertools work in a visual way. Download this cheatsheet Download this cheatsheet
Object detection powers transformative applications, from autonomous vehicles navigating city streets and security systems identifying threats in real time to retail analytics tracking inventory and medical imaging detecting tumors. But choosing the right model for your computer vision project can be challenging, especially with dozens of architectures claiming superiority across different metrics. In this guide, […]
How do you speed up computational Python code? A common, and useful, starting point is: Pick a good algorithm. Use a compiled language to write a Python extension. Maybe add parallelism so you can use multiple CPU cores. But what if you need more speed?
Simon’s been in search of the perfect code sandbox. This article is about his latest attempt and covers why he wants a sandbox and what tech he’s used to achieve it.