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- Python Weekly (Issue 763 September 17 2026)
Python Weekly (Issue 763 September 17 2026)
Welcome to issue 763 of Python Weekly. Let's get straight to the links this week.
100 real-world tasks. 8 tool categories. 1 complete DevOps stack.
Master Git, Docker, Kubernetes, Linux, CI/CD, Terraform, and monitoring through structured tasks across real-job scenarios, and earn a shareable credential that proves you can apply what you’ve learned.
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Articles, Tutorials and Talks
Learn how OpenAI evolved Habitat from a Python library into a globally distributed storage platform serving 1 billion ChatGPT users and 22M requests per second.
Eddie Atkinson makes the case that many teams move from Pandas to distributed systems too early, when single-machine tools like Polars and DuckDB can handle much larger analytical workloads with significantly better performance and memory efficiency in his benchmarks. He highlights lazy execution, multithreading, streaming, spill-to-disk, and Arrow interoperability as reasons these tools can cover many “medium data” workloads without the complexity of distributed systems.
Philipp Schmid demonstrates how improving LLM capabilities allow developers to replace complex Python agent orchestration with simple instructions and skill files (AGENTS.md and SKILL.md) running inside hosted sandboxes. By building the same GitHub pull request review agent three times, he shows why developers should simplify agent architectures, rely on general-purpose tools, and focus on instructions and evaluations instead of maintaining unnecessary orchestration code.
The article explores 4×4 Sudoku, showing that there are 288 valid solutions, which reduce to just 12 structurally distinct grids when digit permutations are ignored. Using Python brute force and a clever bitmask technique for checking minimal subsets, the author identifies 85,632 minimal puzzles (3,568 up to digit permutations), each with a unique solution that becomes non-unique if any given digit is removed.
Python 3.15 is almost here! This video breaks down 10 of the most interesting new features and changes coming to Python 3.15, using real Python code and visual explanations.
The article explains when to use re.prefixmatch(), re.search(), and re.fullmatch(), what soft deprecation means for existing code, and how to flag re.match() usage with Ruff.
The author details building an MLIR backend for Python's Finch sparse tensor compiler, covering type conversion, buffer management, SSA values, control-flow lowering, and SciPy sparse interoperability. Benchmarks show MLIR outperforming Numba on most tested kernels and beating SciPy on more complex sparse operations, while highlighting current limitations and plans for GPU support.
Adam Johnson explains how to implement the standard /.well-known/change-password URL redirect in Django using RedirectView, helping password managers direct users to their password change pages. He also covers testing the endpoint, checking for server misconfigurations that could interfere with password managers, and setting the correct autocomplete attributes on password fields.
Ben Tasker shares his experience customizing the Badgeware Badger—a Raspberry Pi RP2350-powered e-ink name badge running MicroPython. He details troubleshooting image formatting, color-depth, and font sizing issues to display his custom avatar, a Fediverse social handle, and logo on the device.
The video demonstrates building a polling application with FastHTML, covering form creation, POST request handling, and storing polls in a database. It also shows how to define database models using Python data classes, create a voting page, and process submitted votes.
NumPy in the browser now uses OpenBLAS through Emscripten-forge, accelerating matrix multiplication by up to 30.92× for float32 and 14.90× for float64 in the authors’ 1024×1024 benchmarks. The article details the WebAssembly and Fortran toolchain work behind the improvement, plus upcoming SIMD optimizations that push performance further, reaching up to 64.89× with optional Relaxed SIMD in the same benchmark
Distributed systems synchronise themselves without being told to: every client that retries on a fixed interval, restarts after a deploy, or reconnects after a network blip ends up firing at the same moment as every other client. The resulting spike is a self-inflicted denial of service that no single component is responsible for. Adding randomness to any repeating interval breaks the alignment, and it is usually one line of code against a failure mode that otherwise requires capacity you never needed.
Interesting Projects, Tools, and Libraries
Removes 20+ patterns of AI slop from any piece of writing.
Cayu is a production agent runtime for building and operating AI agents in Python.
Open-source Python server emulator that restores online multiplayer functionality for Need for Speed: Underground 2, Most Wanted, and Carbon by recreating the games’ original backend services.
Engrim is a local-first Python memory layer for coding agents that preserves project context and decisions across tools like Claude Code, Cursor, Codex, Windsurf, and OpenCode.
Claude-Red is a curated library of drop-in Claude Skills that equips Claude with specialized offensive-security workflows for areas like web apps, Active Directory, wireless, exploit development, cloud, and AI security.
Agent-driven research knowledge base. Agents collect, search, and synthesize web research into a persistent, searchable wiki.
ZeroModels is an open-source Keras 3 library of pretrained models spanning vision, language, speech, depth estimation, and multimodal tasks.
LintLang is a Python-based static analyzer that catches ambiguous, conflicting, and error-prone instructions in AI agent prompts, tool descriptions, and configuration files.
PyTorch implementations of modern open-source LLM architectures (Llama, Qwen, DeepSeek, Gemma, GPT-OSS, Kimi, and more) — written from scratch for readability and learning, based on Sebastian Raschka's LLM Architecture Gallery.
Flowsint is an open-source OSINT graph exploration tool designed for ethical investigation, transparency, and verification.
E-ink bird frame for Raspberry Pi - real-time bird detection by audio, fully local AI, rendered as real, hand-cut 1800s bird illustrations.
Independent Auditing of AI Agents. Run by human or the agent itself, to answer the most crucial question in the AI Agent Economy. Is the agent doing what is supposed to do? With iFixAi you can have this answer in less than 120 seconds.
A declarative YAML syntax around SQL for making dashboards.
New Releases
JupyterHub 6.0 introduces stricter named-server rules, more granular API permissions, improved spawn-failure reporting, and internal HTTP communication via aiohttp. The release also raises the minimum Python version to 3.10, replaces StatsD metrics with Prometheus, and includes a database schema upgrade that requires a backup before updating.
Upcoming Events and Webinars
There will be following talks
Generative Computing with Mellea: Programs That Call Models Like Functions
FactReasoner: Porting a Probabilistic Factuality Assessor to Mellea
The Mellea Skills Compiler: From Plain-Language Specs to Governed Pipelines
There will be following talks
Building a super 8 film scanner at home
Intuitive understanding for Survival Models
Training an Agent to Play the Pokémon TCG Using Reinforcement Learning
How to learn AI?
There will be following talks
Everything You Don't Need to Know About Classes in Python
Why Your AI Agent Is Only as Good as Your Data
There will be following talks
Mapping the PyData Community with Python and Web Scraping
City Dreams - Can GenAI go beyond slop?
There will be a talk, Build Your First API with Python!
There will be following talks
Empathy Data in LLM's - Part II
The meaning of lift - understanding marketing effectiveness
There will be a talk, The Cloud won't die. It's monopoly on intelligence might.
There will be following talks
The LLM Stack: Inference, Accelerators, Open Models and Beyond
Getting Started with Self-Hosted LLMs - Miguel Silva (AWS)
There will be a talk, Building Production-Ready AI: Agentic Systems, RAG & Responsible AI.
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