Django: profile memory usage with Memray

Use Memray to profile Django startup, identify heavy imports like numpy, and reduce memory by deferring, lazy importing, or replacing dependencies.

Asyncio Is Neither Fast Nor Slow

There are many misconceptions on asyncio, as such there are many misleading benchmarks out there. This article looks at how to analyse a benchmark result and to come up with more relevant conclusions.

Nothing to Declare: From NaN to None via null

Explore the key differences between NaN, null, and None in numerical data handling using Python. While all signal “no meaningful value,” they behave differently. Learn about the difference and how to correctly handle the data using Pydantic models and JSON serialization.

The Hidden Cost of Python Dictionaries

Learn why Python dicts cause silent bugs and how NamedTuple, dataclass, and Pydantic catch errors earlier with better error messages.

I Created a Game Engine for Django?

Multiplayer Snake implemented in Django using Django LiveView, 270 lines of Python, server side game state, WebSocket driven HTML updates, no custom JavaScript.

Golfing APL/K in 90 Lines of Python

Fun With Mypy: Reifying Runtime Relations on Types

This post describes how to implement a safer version of typing.cast which guarantees a cast type is also an appropriate sub-type.

How to Switch to ty From Mypy

The folks at Astral have created a type checker known as “ty”. This post describes how to move from Mypy to ty, including in your GitHub Actions.

Python: Tprof, a Targeting Profiler

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Migrating From Celery to Django Tasks

Django’s new Task Framework makes it surprisingly easy to replace Celery, covering configuration, task migration, queues, workers, and periodic jobs with simpler, built-in tooling.

Recursive structural pattern matching

Learn how to use structural pattern matching (the match statement) to work recursively through tree-like structures. In this short article you will learn to use structural pattern matching in recursive, tree-like data structures. The examples from this article are taken from a couple of recent issues of my weekly newsletter.

Python 3.14 vs. 3.13 / 3.12 / 3.11 / 3.10

WebAssembly as a Python Extension Platform

Python⇒Speed: Unit testing your code's performance, part 1: Big-O scaling

Python 3.6-3.14 Performance

One of the maintainers of Knave has been tracking Python performance data for a while and a recent upgrade of one of their machines meant they now had more info across different hardware. This post compares their performance test across Apple M1 & M5, Zen2 and Cascade Lake chips.

Django Quiz 2025

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DOOM in Django: testing the limits of LiveView at 600.000 divs/seconds

Yep, DOOM in Django. That's right. Django LiveView streams ViZDoom as 100x100 pixel frames mapped to 10,000 divs at 60 FPS, sustaining about 600000 divs per second reliably.

PyPI in 2025: A Year in Review - The Python Package Index Blog

PyPI strengthened security and organization features in 2025, adding trusted publishing, attestations, improved 2FA, malware response, and organization management enhancements.

A (biased) Pure Python Performance Comparison

The following is a performance comparison of several (pure) Python implementations, for a large part of the Shed Skin example set. I left out some of the examples, that would result in an unfair comparison (mostly because of randomization), or that were too interactive to easily measure. Obviously this comparison is very biased, and probably unfair in some way to the other projects (though I've tried to be fair, for example by letting PyPy stabilize before measuring).

Choosing the Right Python Docker Image for Finance Workloads