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- Python Weekly (Issue 734 February 26 2026)
Python Weekly (Issue 734 February 26 2026)
Rahul Chaudhary
February 26, 2026
Welcome to issue 734 of Python Weekly. Let's get straight to the links this week.
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News
Type expressions produce runtime type form objects that enable introspection, runtime type checking, and metaprogramming, but there is currently no precise way to type annotate functions that accept them. PEP 747 introduces typing.TypeForm to solve this gap for static type checkers, without changing Python’s grammar or runtime behavior.
Articles, Tutorials and Talks
Learn how the author used game engine tech to solve a video streaming problem.
The video explains when to use a property versus a method in Python, highlighting how each communicates different expectations around cost, side effects, and behavior. It also argues that async properties are typically a design smell and shows how to keep asynchronous work explicit while maintaining clean object design.
The article explains that when you ask an AI coding agent to fix a bug, it often makes unnecessary changes to working code because it does not explicitly know which behavior should stay the same and which should change, leading to regressions. It then proposes a property-aware code evolution approach that defines a clear bug condition and corresponding preservation properties so the fix can be both correct and surgical, constraining the AI’s changes with explicit, testable boundaries.
This post explains how plot capture works differently in Python and R: in Python (especially Jupyter) all figure output goes through a single display system that makes automatic capture reliable, whereas in R the graphics architecture has no central display layer so capturing plots is harder and often requires explicit publishing. It contrasts the architectural reasons behind these differences and shows why GoFigr’s automatic plot capture succeeds more easily in Python but needs explicit calls in R for full coverage.
A practical step-by-step guide to using Django ORM in standalone mode to connect to and query an existing database using inspectdb.
An overview of OAUTH in non-web apps and the general data-flow of an OAUTH Client.
The article shows how to build a full Retrieval-Augmented Generation (RAG) system where .NET Aspire orchestrates a Python backend, Qdrant vector store, and Ollama LLM, letting everything run together with a single command and unified observability. It walks through the architecture and pipeline for ingesting data, performing hybrid retrieval, and serving grounded answers in a frontend, highlighting how Aspire simplifies polyglot service orchestration and telemetry.
This Python course will help you master the technical stack behind autonomous intelligence. This course is designed to take you from the core syntax of Python through the complexities of data handling and API integration, culminating in the deployment of sophisticated Large Language Models. You will learn to write code and to architect systems that can reason, use tools, and solve real-world problems using both proprietary and open-source AI.
The post explains that Python 3.15’s new lazy imports feature is great for CLI tools, but naïve argparse subcommand patterns accidentally force all lazy imports to execute early, negating the benefit. It then shows two simple patterns that preserve laziness for subcommands: dispatching with a match statement, or wrapping lazy-imported callables in lambdas so they are only reified when the chosen subcommand actually runs.
After shelving the idea for four years, the author used AI to rapidly bootstrap Coodie, a Pydantic v2 based ODM for Cassandra and ScyllaDB inspired by Beanie for MongoDB. The result is a fully working library with declarative schemas, automatic table management, sync and async APIs, and chainable queries, built largely with AI assistance in a fraction of the time.
Interesting Projects, Tools, and Libraries
Python functions powered by AI agents - with runtime post-conditions for reliable agentic workflows.
A centralized dashboard for managing Django admin panels.
Stario is a Python web framework for real-time hypermedia. While most frameworks treat HTTP as request → response, Stario treats connections as ongoing conversations - open an SSE stream, push DOM patches, sync reactive signals.
Eventum produces synthetic events and delivers them anywhere in real time or in batch. Generate a continuous stream of logs for your SIEM, seed a staging database with realistic data, or stress-test a pipeline with high-throughput traffic.
Thread synchronization utilities for Python.
Plug-and-play multi-object tracking for any detection model.
A Fun, Live View of SSH Break-in Attempts.
Rust-powered WebSocket engine for Python. Up to 14M msg/s JSON, 30M msgpack/zlib fan-in, 2.1M del/s fan-out, zero-GIL JWT, E2E encryption.
Extract a slice of your production database to reproduce bugs locally. Point dbslice at a record, it follows foreign keys and gives you a complete, importable subset.
A free and open-source platform that unites the best signal intelligence tools into a single, accessible interface.
Upcoming Events and Webinars
There will be following talks
Agentic Workflows for Data Engineers: How AI Agents Are Changing Our Daily Work
Github Codespaces for Data Analysis with Python
There will be a talk, The Hidden Power of Synthetic Data: Teaching Models What Real Data Can’t.
There will be a talk, Machine Learning Adventure in collaboration with Enablis.
There will be following talks
Grammar of Graphics - Bringing Sanity to Data Visualisation
In Praise of Documentation: Tools, Tips & Techniques for Literate Programming in the AI Age
From Zero to GPT: How Large Language Models Actually Work
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