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- Python Weekly (Issue 728 January 15 2026)
Python Weekly (Issue 728 January 15 2026)
Rahul Chaudhary
January 15, 2026
Welcome to issue 728 of Python Weekly. Let's get straight to the links this week.
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News
Anthropic has entered a two-year partnership with the Python Software Foundation, contributing $1.5 million to bolster the language's core infrastructure and ecosystem security. The investment specifically targets the development of automated tools for proactive malware review on PyPI and supports the ongoing work of the CPython Developer in Residence.
Articles, Tutorials and Talks
In this series, we'll be learning how to build a full-featured web application from the ground up using the FastAPI framework in Python. We'll build both a JSON API for programmatic access and HTML pages for users to browse in the browser. Throughout the series, we'll set up a database with SQLAlchemy, create Pydantic models for data validation, and implement complete CRUD operations. We'll add user registration and login with secure password hashing and JWT tokens, handle file uploads for profile pictures, use background tasks for sending emails, and organize our code properly with routers.
Henry Schreiner and Damian Shaw significantly improved the performance of the Python packaging library by using new profiling tools to eliminate redundant regular expressions. The update delivers speed increases up to 5x for version filtering which helps resolve dependencies much faster within the Python ecosystem.
Mihail Eric argues that the core functionality of complex AI coding tools like Claude Code or Cursor is not magic but rather simple agentic logic. He demonstrates that these systems can be replicated in about 200 lines of Python by focusing on the essential loop of reading files, editing code, and executing shell commands.
Arjan argues that long-term code maintainability with AI requires developers to act as high-level architects, constantly refining design patterns and decoupling responsibilities rather than accepting the first "working" output. The video demonstrates this iterative process by showing a real interaction with ChatGPT, where he systematically guides the AI to replace messy if-statements with command patterns and eliminate circular dependencies.
Luis Cardoso breaks down the technical differences between containers, microVMs, gVisor, and WebAssembly to help developers choose the right isolation boundary for running untrusted AI agents. He emphasizes that a secure sandbox requires a clear distinction between the isolation boundary, the access policy, and the lifecycle management of the environment to prevent malicious code from compromising the host system.
Google has introduced the Universal Commerce Protocol (UCP), an open-source standard designed to enable seamless "agentic commerce" across AI platforms like Gemini and Search. The protocol eliminates integration bottlenecks by providing a unified abstraction layer for discovery and transactions, allowing AI agents to handle the entire shopping journey from product search to secure payment.
Savannah Ostrowski highlights sys.remote_exec() as a game-changing feature in Python 3.14 that allows developers to execute scripts inside a running process without a restart. She leverages this capability in a new tool called debugwand to enable zero-preparation remote debugging for Python applications running in Docker and Kubernetes.
Jo Chen argues that high-performance RAG systems require specialized retrieval pipelines for different data types, such as contextual prefixes for technical manuals and whole-document summaries for blog content. The post details a multi-layered defense strategy involving hybrid search, LLM reranking, and aggressive caching to improve accuracy while managing the high costs of production AI.
PyTorch has introduced torchforge and Weaver, a new open-source stack designed to simplify and scale reinforcement learning for large language models across hundreds of GPUs. The system uses Weaver to provide reliable reward signals without human annotations, while torchforge provides the native primitives to manage complex distributed coordination and fault tolerance.
John Voorhees describes building a transcription pipeline that combines deterministic Python scripts for initial cleanup with Claude's "fuzzy" reasoning to catch obscure phonetic errors. The system uses a feedback loop where Claude identifies misspellings like "goti" for "GOTY" and automatically updates a persistent dictionary to ensure the bot becomes more accurate with every episode.
This video today is a crash course on Seaborn, a data visualization library for Python built on top of Matplotlib. It makes it super simple to create statistical graphs of datasets.<br>
Interesting Projects, Tools, and Libraries
Memory infrastructure for LLMs and AI agents.
A Foundation Model for Generalist Gaming Agents.
GrantFlow.ai is a platform for creating grant applications using ML and AI.
LLM reliability research from Hassana Labs.
Kernel Library for LLM Serving.
An improved implementation of the Ralph Wiggum technique for autonomous AI agent orchestration.
Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models.
Simple, lightweight task scheduler for Python with async support, timezone handling, cron expressions, and a beautiful real-time dashboard.
Seapie is a REPL-centric Python debugger that allows for live code manipulation and state inspection through a standard interactive prompt.
Real-time stock market data in your terminal.
BeatBoss is a desktop music player built with Python and Flet.
Jetbase is a simple, lightweight database migration tool for Python projects.
Upcoming Events and Webinars
Learn how MicroPython makes embedded development approachable for IoT, robotics, and visio systems, plus a live demo showing how quickly you can control microcontrollers with code.
There will be following talks
From Grape Stomping to Gorilla Trekking: AI-Driven Discovery of Novel Interests at GetYourGuide
Deep dive into data streaming security
There will be a talk, Quantum computing with Python.
There will be a talk, Macro Investor AI Agent.
There will be a talk, Intro to HTMX & Datastar with Python.
There will be following talks
Augment Your Data: Leveraging LLMs for Expanding Training Sets
Marimo: Build a python demo like a pro
There will be following talks
From Demos to Deployed: Building AI Systems That Work, and Work Right
Building AI Right: Ethics and Implementation in Practice
There will be a talk, Seeing in the Dark: Unlocking AI in Next-Gen Radar Applications.
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