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News & links about Python programming.
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Django 6.1 released

Django 6.1 is now available with features including model field fetch modes, database-level ForeignKey delete options, and dictionary-based email settings. Django 6.0 has ended mainstream support and will receive only security and data-loss fixes until April 2027.

https://www.djangoproject.com/weblog/2026/aug/05/django-61-released/
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Categorization with NLP

A practical look at building grocery categorization without machine learning, using NLP techniques such as stemming, n-grams, syllable splitting, and spell checking. The author shows how a hand-crafted Python algorithm handles messy real-world inputs and edge cases when training data is scarce.

https://softwaremaniacs.org/blog/2026/07/30/categorization-with-nlp/en/
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How to Conquer Concurrency in Python

A practical guide to Python concurrency that builds from OS fundamentals, processes, threads, race conditions, and the GIL to choosing between asyncio, threading, and multiprocessing. It also explains concurrency vs. parallelism, CPU vs. GPU tradeoffs, and how profiling and strong mental models help avoid common performance mistakes.

https://www.youtube.com/watch?v=chrOym38pw4
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Wheels, Bottles and Images

Simon Willison packaged a Go CLI inside Python wheels, showing that PyPI can distribute platform-specific binaries containing no Python at all. The article compares wheels with Homebrew bottles and OCI images, highlighting their shared model of immutable artifacts and client-side platform selection, along with growing convergence in registry infrastructure and Sigstore attestations.

https://nesbitt.io/2026/07/30/wheels-bottles-images.html
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Gleam for Python Programmers

A practical guide to Gleam for Python programmers, explaining its syntax, static typing, immutability, pattern matching, and functional programming model through Python comparisons. It gives Python developers a quick way to understand how Gleam differs while building on concepts they already know.

https://third-bit.com/gl4py/
Python: how time-machine is O(1) where freezegun is O(n)

A benchmark shows Python’s time-machine library stays O(1) when mocking time, while freezegun scales O(n) with the number of loaded module attributes and becomes dramatically slower as projects grow. The difference comes from time-machine swapping CPython function pointers directly, while freezegun scans loaded modules to replace references to date and time functions.

https://adamj.eu/tech/2026/08/03/python-time-machine-o1-freezegun-on/
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Celery: from first task to advanced recipes

The article introduces Celery, a distributed task queue for Python, then walks through practical patterns for running, routing, batching, timing out, and retrying asynchronous tasks. It also covers advanced recipes such as preventing parallel execution with Redis locks and integrating Celery tasks with Python’s async/await workflows.

https://sgolev.github.io/blog/2026-07-28-celery-recipes/