12K+ JPEGs From NASA’s Artemis II Mission

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.

Git for Data Scientists

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.

What Every Dev Should Know About the CPython ABI

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.

Best Django Redis configuration for speed and size

`lzma` compresses the most and `zlib` is about as fast as `zstd` in `django_redis` as compressor.

Gleam for Python Programmers

What Every Python Developer Should Know About the CPython ABI

Python 3.15’s Ultra-Low Overhead Interpreter Profiling Mode

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.

itertools cheatsheet

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

Best Object Detection Models for Machine Learning in 2026

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, […]

6× faster binary search: from compiled code to mechanical sympathy

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?

Store Extra Data for Objects in a WeakKeyDictionary

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Write a coding agent from first principles: better tools

Migrate From mypy To ty And pyrefly

Python in a Sandbox With MicroPython and WASM

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.

The Fastest Python Struct?

Free-threaded Python: past, present, and future

Explicit Lazy Imports Are Coming to Python 3.15

Run Modified Python Code Using the AST Module

How to work with Python’s Abstract Syntax Tree (AST), a foundation of many metaprogramming techniques, and how this can be valuable in the age of AI

PyPy: A new benchmark runner for PyPy

Skip Jupyter’s Hidden State: Reactive Notebooks With Marimo

Marimo is a reactive Python notebook designed to make data science workflows more reproducible. This article shows how it avoids hidden execution state, saves notebooks as plain .py files for cleaner Git diffs, isolates dependencies with uv, supports pytest cells, and exports notebooks into reusable formats including scripts, HTML, and WASM dashboards.