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Human-curated Python news, projects, articles & tools.

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Popular Python Features You Probably Shouldn’t Use

Explore seven powerful Python features, including properties, dataclasses, caching, decorators, async, pattern matching, and slots, that can make code harder to understand and maintain when misused. Learn the trade-offs of each feature and when using it improves code clarity, design, and maintainability.

https://www.youtube.com/watch?v=RHCyTWaRZSk
Django: check version support with norwegianblue

Adam Johnson demonstrates how to use norwegianblue, a command-line tool powered by endoflife.date, to check release cycles and support deadlines for Django, Python, PostgreSQL, and other software. The tool displays version compatibility, end-of-life dates, and upcoming support deadlines in a color-coded table, helping developers identify unsupported versions and plan upgrades.

https://adamj.eu/tech/2026/10/16/django-norwegianblue/
Python's binary wheels and supply chain attacks

Artem Golubin highlights a supply chain security risk in Python's packaging ecosystem: binary wheels uploaded to PyPI can contain malicious code that differs from their accompanying source distributions. Since most security scanners focus on source code rather than compiled binaries, attackers could hide malware in prebuilt wheels that pip installs by default.

https://rushter.com/blog/python-binary-wheels/
Write Python, Run native, All in Python.

PCC compiles Python and C source into native programs. This compiler and runtime aims to preserve Python's syntax and behavior without libpython, with self-hosting。

https://github.com/allstoalls/pcc
apache iceberg beneath the surface

A video tutorial on how Apache Iceberg's manifests, snapshots, and catalog turn a folder of Parquet files into a table with ACID transactions, hidden partitioning, time travel, and schema evolution. Tutorial uses the data on NYC taxi trips along with PyIceberg, Polars, and DuckDB.

https://www.youtube.com/watch?v=9Fz5noobWcE
Build an AI That Turns Your Voice Into Images, Video and Audio (Python)

This video demonstrates how to build a Python AI agent that automates the entire video creation pipeline, including speech-to-text, scriptwriting, image generation, voice synthesis, and video rendering. By using OpenRouter as a unified API, the project shows how to integrate multiple AI models efficiently while tracking individual operation costs.

https://www.youtube.com/watch?v=aypkvuGFq5Y
Bringing uv and ty together

Astral introduces experimental integration between uv and ty, enabling dependency-aware type checking for Python scripts with PEP 723 inline metadata and a new uv check command that automatically prepares project environments and runs ty. The integration also supports workspace-wide type checking and consistent ty versions through uv's lockfile, bringing Python development tools closer t...

https://astral.sh/blog/uv-ty-integration
How to Run Turing-Complete Malware in PyTorch's Backward Pass

Elon Litman demonstrates how a malicious PyTorch loss function can report a value of zero while secretly manipulating model weights through the backward pass. By exploiting differences between floating-point arithmetic and automatic differentiation, he shows how gradient descent can execute arbitrary computations inside a neural network, creating a potential attack vector for AI training...

https://elonlit.com/scrivings/how-to-run-turing-complete-malware-in-pytorchs-backward-pass/
django-lifecycle - hooks for Django models (Signals alternative)

Learn how to usedjango-lifecycleas an alternative to Django signals, with declarative hooks that run when models are created, updated, deleted, or saved. The package also supports conditional hooks that trigger only when specific fields change or transition between particular values.

https://www.youtube.com/watch?v=sNdopNFOEB8
Decoding base64 in Python at 10 gigabytes per second

Daniel Lemire introduces fastpybase64, a Python library built on SIMD-accelerated simdutf that provides a drop-in alternative to Python's standard base64 module. In single-threaded benchmarks decoding Base64 data containing line breaks, it achieves over 13 GB/s throughput, approximately 27 times faster than the standard library and four times faster than pybase64.

https://lemire.me/blog/2026/10/17/decoding-base64-in-python-at-10-gigabytes-per-second/