A fully open source framework for creating RL training swarms over the internet.

https://github.com/gensyn-ai/rl-swarm

Optimize your Dockerized Flask or Django applications by switching from pip to uv for faster and more efficient Python package management. This guide provides a step-by-step approach to streamlining your development workflow.
https://nickjanetakis.com/blog/switching-pip-to-uv-in-a-dockerized-flask-or-django-app

Daytona is a Secure and Elastic Infrastructure for Running AI-Generated Code

https://github.com/daytonaio/daytona

A collaborative note taking, wiki and documentation platform that scales. Built with Django and React.

https://github.com/suitenumerique/docs

This article gives an early look at Astral's new static type checker, Red Knot, and documents the process of compiling and running this work-in-progress tool. The author, Michael Jurasovic, even uses it on several large codebases to test its speed against mypy.

https://jurasofish.github.io/a-very-early-play-with-astrals-red-knot-static-type-checker.html

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

Fully local web research and report writing assistant

https://github.com/langchain-ai/local-deep-researcher

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

Agent S: an open agentic framework that uses computers like a human

https://github.com/simular-ai/Agent-S

Lightweight Pandas monkey-patch that adds async support to map, apply, applymap, aggregate, and transform, enabling seamless handling of async functions with controlled max_parallel execution.

https://github.com/telekinesis-inc/aiopandas

Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead.

https://github.com/PrunaAI/pruna

A simple tool that let's you explore different possible paths that an LLM might sample.

https://github.com/willkurt/token-explorer

2X faster ASGI web framework for python, offering high-level development, low-level performance.

https://github.com/raceychan/lihil

Official PyTorch implementation of TokenSet.

https://github.com/Gengzigang/TokenSet

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