Python notes – Telegram
This blogpost from marimo explains how their notebook file format, which stores notebooks as Python programs instead of JSON, makes them more reusable, version-friendly, and testable. The format is designed to treat notebooks as proper software artifacts, overcoming the limitations of traditional .ipynb files.

https://marimo.io/blog/python-not-json
This piece provides a guide to building a Retrieval-Augmented Generation (RAG) system using Anthropic's Claude, PostgreSQL, and Python on AWS. The tutorial walks through setting up the necessary PostgreSQL extensions and using Amazon Bedrock to create an application that generates more accurate AI responses.

https://www.tigerdata.com/blog/building-a-rag-system-with-claude-postgresql-python-on-aws
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This piece introduces Unvibe, a Python library that uses unit tests as a reward function to guide Large Language Models in generating correct code. The author, Claudio Santini, explains that this approach works well for generating complex code within large, existing codebases, provided they are well-tested.
https://claudio.uk/posts/unvibe-a-python-test-runner-that-generates-correct-implementations.html
Dan LaManna's latest post reflects on a long-standing architectural issue within the Django framework that has recently been addressed. The author celebrates the solution, marking a significant improvement for the popular web framework.
https://danlamanna.com/posts/rest-in-peace-djangos-framework-problem/
This article from Pybites offers a practical look at using the uv tool to streamline Python package development and testing. It walks through the setup and commands needed to integrate this fast new tool into your workflow.
https://pybit.es/articles/developing-and-testing-python-packages-with-uv/
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