Python Weekly (Issue 761 September 3 2026)

Author

Rahul Chaudhary
September 03, 2026

Welcome to issue 761 of Python Weekly. Let's get straight to the links this week.


AI can build faster. Can your team decide better?

AI can draft the PRD and prototype the idea. Jira Product Discovery helps teams decide whether it belongs on the roadmap. Bring feedback and ideas together, prioritize as a team, and keep your roadmap connected to delivery in Jira.


News

The 2026 Django Developers Survey captures how nearly 3,500 developers use Django, covering frameworks, databases, testing, deployment, tooling, and development practices. AI is now deeply embedded in Django development, with 58% using it daily and Claude Code emerging as the most widely used AI coding tool.


Articles, Tutorials and Talks

Six common OOP practices can make code harder to maintain, including overusing inheritance, building unnecessary class hierarchies, breaking the Liskov Substitution Principle, and abstracting too early. The video explains why these patterns backfire and shows simpler alternatives that keep object-oriented code more flexible, understandable, and maintainable.

Wrapture takes a different approach to Python unit testing by wrapping real code instead of replacing it with mocks, preserving real arguments, return values, call structure, and signatures. It makes it easier to test internal calls, inject failures, verify call order, and assert what did or did not happen without losing the behavior of the code under test.

When should you return NotImplemented from a dunder method? Why not return False or raise an exception instead?

The post explains the four caching layers in LLM stacks: KV cache, prefix caching, prompt caching, and semantic caching, covering what each stores, their trade-offs, interactions, and the most common issues that prevent reuse. It includes first-principles explanations, code demos (with transformers and vLLM-style logic), production pitfalls, and practical takeaways for reducing recomputation costs and latency.

The author traces a simple np.add(a, b) call through NumPy’s internals, from Python argument handling and dtype dispatch to iteration strategy and the final SIMD-optimized C loop. Along the way, the post explores ufunc overrides, type promotion, dispatch caching, memory layout, GIL release, and CPU-specific optimizations that determine how NumPy actually performs an addition.

Build a fully functioning AI agent from scratch in pure Python, without frameworks, third-party tools, or vibe coding. The course focuses on the core mechanics behind agents so you understand how they work under the hood, not just how to prompt existing tools.

In this video, we'll look at Django API options, exploring the best modern packages for API-driven development in Django. We will take a look at Django REST Framework, django-ninja, django-bolt, and django-modern-rest - looking at the pros/cons of each.

Nifty Django Feature: Use Index for Custom Migration Operations

Part of the Nifty Django features series: You can hack Django's Index class to define custom migration operations on your model.

Learn how to use these models, how to fine-tune them on diverse, specialized datasets that look nothing like their training data, and how to evaluate the results – all within PyCharm.


Interesting Projects, Tools, and Libraries

AI-powered virtual executive team, a single coherent executive persona backed by 8 specialist Claude agents (FastAPI + Next.js).

Open-source personal finance manager. Self-hosted, privacy-first.

RL training environments for Microduck (mjlab)

Daily updated list of Tech Company OAs and Interview Problems. Save your time from finding them all over the internet.

Desktop flight and marine radar: a real-time aircraft and marine vessel tracker powered by a Raspberry Pi and 4" round screen.

Weather, tides, the sun, the moon, and maps, in your terminal. The Old Farmer's Almanac meets Minitel.

An open source model gateway that provides one control plane across closed, open-source, local, and custom models.

Automatic Harness Optimization with Durable Updates from Agent Execution Traces.

Library built for generating semantic layer graphs for query routing, query generation and data discovery.


Upcoming Events and Webinars

There will be following talks

  • Generating Realistic Synthetic Data with Python

  • Harnessing the Power of Python for Data Analytics

There will be a talk, When Logs Become Data: Building a PostgreSQL Logging Handler in Python.

There will be a talk, Accelerating Enterprise Modernization with Agentic AI: Inside AWS Transform.


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