Welcome to issue 703 of Python Weekly. Let's get straight to the links this week. |
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Articles, Tutorials and Talks |
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This five-part video series demonstrates how to build a robust, modern Python API with Azure Cosmos DB, covering topics from data modeling with Pydantic and async operations in FastAPI to batch processing and centralized error handling. Each episode provides practical code examples and best practices for scalable, production-ready applications using Cosmos DB and Python. |
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The article shares practical strategies for using Claude as an AI coding assistant to ship real production code, emphasizing the importance of clear documentation, structured prompts, and keeping humans in the loop for critical decisions and tests. It demonstrates how combining rigorous development practices with AI can dramatically accelerate productivity, illustrated by a case study refactoring error handling across a large codebase in hours instead of days. |
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The video explores eight unconventional ways to define functions in Python—from lambda and partial functions to decorators, callable classes, and even manual bytecode crafting—revealing how flexible and dynamic Python’s function system really is. Most of these methods are rarely used in practice, but learning them offers deeper insight into Python’s internals and advanced metaprogramming techniques. |
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ColPali is a breakthrough multi-modal RAG system that treats documents as images, preserving tables, figures, and formatting for superior retrieval and question-answering compared to traditional text-only approaches. This post details how to build a ColPali-powered API using PDF-to-image conversion, visual embeddings, a vector database, and a multimodal LLM—enabling accurate, layout-aware answers from complex documents. |
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Writing Django management commands can involve a ton of boilerplate code. But Revsys uses two libraries that cut our management command code in half while making it more readable and powerful: django-click and django-typer. |
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Learn how to speed up and harden your Django Docker builds using Astral’s uv for faster installs, better caching, and reproducible environments. |
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This video demonstrates how to build AI agents using Django for data management, LangGraph for agent coordination, and Permit.io for robust access control, enabling safe, granular, and flexible interactions with user data and APIs. The course walks through integrating these tools step-by-step, empowering developers to create scalable, multi-agent systems with strong permissions and rapid prototyping capabilities. |
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Switched FastAPI endpoints from async def to def and increased the AnyIO threadpool limit to 2000, significantly improving throughput and latency. This approach avoids the complexity of full async while delivering reliable performance gains. |
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The video tutorial demonstrates how to get started with Google DeepMind’s Gemini models using the Google Gen AI Python SDK, walking through API key setup, prompt and chat interactions, and multimodal capabilities like image and audio processing. It also highlights advanced features such as streaming responses and the new Gemini 2.5 thinking models for step-by-step reasoning. |
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Recent frontier AI models are increasingly “reward hacking” by exploiting scoring bugs or task environments to achieve high scores without solving problems as intended, despite often recognizing these actions are misaligned with user goals. This behavior raises concerns about AI safety and alignment, as attempts to curb reward hacking may simply drive it underground rather than eliminating it. |
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Interesting Projects, Tools, and Libraries |
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Memory for AI Agents in 5 lines of code. |
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Redis for LLMs - Infinite and Ultra-Fast. |
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Modern admin interface for FastAPI with built-in authentication, event tracking, and security features. |
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Archon is an AI agent that is able to create other AI agents using an advanced agentic coding workflow and framework knowledge base to unlock a new frontier of automated agents. |
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Building blocks for rapid development of GenAI applications. |
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A Model Context Protocol (MCP) server that lets you manipulate Excel files without needing Microsoft Excel installed. Create, read, and modify Excel workbooks with your AI agent. |
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A New and Modern SSH connector written in Python. |
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Coordinate-Free Visual Grounding for GUI Agents. |
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Tokasaurus is an LLM inference engine designed for high-throughput workloads. |
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PGlite wrapper in Python for testing. Test your app with Postgres just as lite as SQLite. |
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PandaAGI provides a simple, intuitive API for building general AI agents in just a few lines of code. |
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Multi-Agents LLM Financial Trading Framework. |
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A Python library for dynamic dispatch based on module versions and backends. |
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A high-performance algorithmic trading platform and event-driven backtester. |
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New Releases |
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Python 3.13.5 is an expedited maintenance release addressing critical bugs in extension module building on Windows, generator expression error handling, and compatibility with int-like objects in random.getrandbits(). Users are encouraged to upgrade for improved stability and reliability in the latest major Python release. |
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Django has released bugfix versions 5.2.3, 5.1.11, and 4.2.23 to complete the mitigation for CVE-2025-48432, further securing response logging by properly escaping untrusted input. These updates strengthen the original security fix and users are encouraged to upgrade as soon as possible. |
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Upcoming Events and Webinars |
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There will be following talks |
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There will be following talks |
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There will be following talks |
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There will be a talk, Leveraging Federated Learning to Build More Robust AI Models for Biomedicine. |
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There will be a talk, Finding Insight on Coral Disease with Tensorflow and Scipy. |
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