Python Weekly (Issue 732 February 12 2026)

Author

Rahul Chaudhary
February 12, 2026

In partnership with

Welcome to issue 732 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

This tutorial walks through rebuilding OpenClaw, the open source AI agent, from the ground up, starting with a 20 line Telegram bot and ending with a fully functional mini version in about 400 lines, so you can truly understand the architecture by building it yourself.

The video walks through refactoring a small Python animation API from a clunky configuration style into a fluent interface that reads like a story using method chaining and domain specific methods. It also explains what the fluent interface pattern is, how it differs from the builder pattern, where it already appears in Python, and when it is appropriate or inappropriate to use.

Learn how Pydantic uses inline-snapshot and dirty-equals for better testing.

Learn about the recent performance improvements made to Pyrefly's language server capabilities.

Compare Python 3.12, 3.13, and 3.14 side by side. See what changed across free-threading, JIT, t-strings, performance, and library support plus which version you should actually use in 2026.

Text classification with Python 3.14's zstd module

The post shows how you can use Zstandard compression as a simple yet effective method for text classification by comparing compression sizes across class-specific models, achieving competitive results without traditional machine learning. It walks through an implementation using Zstd dictionaries and explains why compressed length differences can serve as a strong signal for categorizing text into labels.

Eli describes how he used an LLM coding agent (Codex) to help rewrite the popular Python C parser pycparser, replacing its old PLY based parser with a hand-written recursive descent parser that passes the full test suite and is easier to maintain. He found the experience efficient and insightful, noting the agent did most of the heavy work in far less time than doing it manually while highlighting how strict tests and clear goals make such collaborations effective.

Charles Leifer introduces cysqlite, a from scratch DB API compatible SQLite driver he revived after pysqlite3 became harder to maintain and less compelling long term. The main focus is sane transaction handling that matches SQLite’s default behavior, avoids Python sqlite3’s confusing autocommit and legacy modes, and provides a cleaner base for SQLite extensions and Peewee integration.

A zero-cost library to integrate Webmentions in your website.

Creating Reddit Summaries with URL Context and Gemini

Raymond Camden demonstrates using the Gemini API's "URL Context" tool to scrape and summarize Reddit threads without relying on Reddit's restricted APIs. He also shares a "double-prompting" workaround to convert the unstructured URL summaries into a precise JSON format, since Gemini currently disables structured output when using the URL tool.

The post shows how to build and use custom skills in the OpenAI API to extend a model’s capabilities with structured, reliable functions that handle specific tasks like lookup or reasoning. It explains how to define, register, and invoke these skills in requests, how they interact with agents, and best practices for building them into workflows.

The post is a hands-on guide explaining why the author migrated his Python web applications to Docker containers, highlighting Docker’s consistent isolation and practical benefits for deployment compared to traditional setups like system Python and virtual environments. It walks through what to include in the container (base image, Python, virtual environment, and WSGI server such as gunicorn), offers example Dockerfiles and configuration files, and discusses how to serve static files with host nginx while keeping databases outside of Docker.


Interesting Projects, Tools, and Libraries

Train and inference GPT in 243 lines of pure, dependency-free Python.

A Zero-Code Multi-Agent Platform for Developing Everything.

Autonomous multi-agent coding framework that plans, builds, and validates software for you.

Teaching tool and debugging aid in context of references, mutable data types, and shallow and deep copy.

An AI Research Science benchmark for quantifying the end-to-end AI research abilities of LLM agents.

The 100 line AI agent that solves GitHub issues or helps you in your command line. Radically simple, no huge configs, no giant monorepo—but scores >74% on SWE-bench verified!

A minimal, secure Python interpreter written in Rust for use by AI.

This project explores data transfer using a screen-to-camera approach. The idea is simple: encode information into a sequence of QR codes, display them as a video on a screen, and then use a camera to capture and decode the video frames to retrieve the original data.

Expanse is a modern Python web application framework focused on developer experience that offers features like transparent dependency injection, a SQLAlchemy-powered database component, and conventions to help build scalable web apps quickly.

Semantic dependency based cache with high performance and concurrency in mind.

PentestAgent is an AI agent framework for black-box security testing, supporting bug bounty, red-team, and penetration testing workflows.

The most powerful local music generation model that outperforms most commercial alternatives


Upcoming Events and Webinars

There will be following talks

  • Unlocking Document Intelligence with Docling

  • From OLAP to AI: How Hudi Brings Vector Search Directly to the Data Lakehouse

There will be following talks

  • Graphical Interfaces in CircuitPython

  • The Other Elses

  • Building RESTful APIs with Python and AEPs

There will be a talk, Python Meets AI Agents.

There will be following talks

  • Open Source & Sovereign Architecture

  • Klarna’s Foundation Model

There will be a talk, Data Pipelines at the Advanced Photon Source.

There will be following talks

  • Traditional Datacenters vs Microsoft AI Superfactory

  • vLLM in Practice: Efficient LLM Inference with Paged Attention


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