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News & links about Python programming.
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Setting Django's DEBUG safely

Deploying Django with DEBUG=True exposes your app to attackers. Learn why it's risky and how to fail closed so DEBUG=False stays the safe default.

https://lincolnloop.com/blog/setting-djangos-debug-safely/
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Projects I Wouldn't Use Python For in 2026

A longtime Python developer reflects on decades of using Python for almost everything, then explains why it is no longer always his first choice and what he often uses instead. It also covers where Python still shines and why it remains an important part of his toolkit.

https://menno.io/presentations/not-python-2026/index.html#/
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LoopX

Lightweight loop engineering state kernel for long-running AI agent teams. Agent-loop agnostic across Codex, Claude Code, and other coding agents, with durable goals, quota-aware auto-wake, executable todos, evidence logs, and verifiable handoffs.

https://github.com/huangruiteng/loopx
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Why dict[str, Any] Slowly Destroys Your Code

A Python booking system is refactored from loose dictionaries into a stronger domain model using dataclasses, enums, and value objects. The video shows where stronger types improve maintainability and where keeping a simple string is still the better choice.

https://www.youtube.com/watch?v=lM7zWJRrRtg
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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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