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- Python Weekly (Issue 738 March 26 2026)
Python Weekly (Issue 738 March 26 2026)
Rahul Chaudhary
March 26, 2026
Welcome to issue 738 of Python Weekly. Let's get straight to the links this week.
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News
OpenAI’s acquisition of Astral brings uv, Ruff, and ty into its Codex ecosystem to expand AI-assisted development beyond code generation into broader workflows. While the tools remain open source, the move has sparked discussion about the future neutrality of core Python infrastructure and its alignment with AI-driven use cases.
A compromised LiteLLM PyPI release injected malicious code that executed on install, stealing credentials and spreading into cloud and Kubernetes environments. The incident highlights how unpinned dependencies and trusted AI tooling create massive supply chain risk, where a single package can silently compromise thousands of downstream systems.
Articles, Tutorials and Talks
Descriptors define how Python resolves attribute access, explaining why values sometimes come from the instance, class, or elsewhere in non-obvious ways. Understanding descriptor rules enables cleaner, more reusable designs by giving you precise control over attribute behavior.
High-performance type checking at Meta required a performance-first architecture and tight integration with developer workflows, enabling fast, incremental analysis at massive scale. The key lesson is that large Python codebases adopt typing successfully through gradual, low-friction tooling that prioritizes developer ergonomics and fast feedback over strict correctness.
How personality clashes, an absent founder, and a controversial redesign fractured one of Python's most popular projects.
The post suggests that heavy LSP and static analysis approaches are unnecessary for many common autocomplete scenarios. It shows a lightweight, pattern-based approach can deliver faster, more responsive suggestions without full semantic analysis.
This post describes using Claude to assist in fixing PyPy 3.11 test failures, with all generated changes run in a sandbox and verified locally. It highlights a practical workflow where AI suggests patches but humans validate results, enabling faster debugging without sacrificing safety.
The article covers a full rewrite of the Akismet Python client to add async support, modern HTTP handling, and a richer response model while preserving usability. It emphasizes API ergonomics, testing support, and maintainability, while honoring the original author and evolving the library for modern Python.
Learn how to extract high-quality data from complex, unstructured PDFs using LlamaParse powered by Gemini 3.1 Pro. This guide demonstrates an event-driven workflow to automate the parsing of dense financial tables and generate intelligent summaries with Gemini 3.1 Flash. Perfect for developers building scalable document-parsing pipelines and AI personal finance assistants.
We'll look at using Pydantic AI to build agent-based workflows, starting with simple fundamentals, and building up to more complex examples that use vector databases, RAG, multi-agent workflows and more.
In this video, we take a quick look at how to easily develop Terminal User Interfaces (TUIs) in Python using a package called blessed.
In this post, we are going to see how the original issue was investigated and how we can leverage bitsets to greatly reduce the memory usage of Pydantic model instances.
This is the second post of the Inside SPy series. The first post was mostly about motivations and goals of SPy. This post will cover in more detail the semantics of SPy, including the parts which make it different from CPython. We will talk about phases of execution, colors, redshifting, the very peculiar way SPy implements static typing, and we will start to dive into metaprogramming.
Custom Django managers can create a false sense of security because their filters only apply to direct queries, not to related or joined models, leading to unintended data exposure. The key lesson is that access control must be enforced explicitly, since relying on managers alone can silently leak data unless you add safeguards or runtime checks.
This tutorial is a comprehensive, end-to-end guide to the Hugging Face ecosystem, showing how modern AI moves from research ideas to real, deployed systems. Rather than focusing on a single model or task, the course presents Hugging Face as the operating system of modern AI—connecting models, datasets, libraries, demos, and deployment into one coherent, practical workflow.
Interesting Projects, Tools, and Libraries
Train the smallest LM you can that fits in 16MB. Best model wins!
One Command Line: Full Automation - agents spawn swarms, delegate tasks, and deliver results.
An event loop for asyncio written in Rust.
ProperDocs is a static site generator intended for project documentation. Source files are written in Markdown and converted to static HTML during the build process.
LocalStack is no longer free. MiniStack is a fully open-source, zero-cost drop-in replacement. Single port · No account · No license key · No telemetry · Just AWS APIs, locally.
Build data transformation pipelines using Python with a visual IDE and AI assistant.
New Releases
After nearly eight years since its creation, Starlette has reached its first stable release. Today, it's downloaded almost 10 million times a day, serves as the foundation for FastAPI, and has inspired many other frameworks. In the age of AI, Starlette continues to play an important role as a dependency of the Python MCP SDK.
Upcoming Events and Webinars
There will be following talks
Build an AI-powered, Sassy Speaking Plant
Designing Delightful CLIs with Python
There will be following talks
Benchmarking Database systems on the NYC Taxi Database
Schema Evolution Automation (SEA)
Using Python Postgres for Async Rag Backfilling
There will be following talks
Deploying AI resources using IaC with Pulumi
Building Instead of Buying: Engineering a Scalable Engine from the Inside
Keep Calm and Trust the AI
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