PythonHub

Writing N-body gravity simulations code in Python https://alvinng4.github.io/grav_sim/5_steps_to_n_body_simulation/

The first year of free-threaded Python A recap of the first year of work on enabling support for the free-threaded build of CPython in community packages. https://labs.quansight.org/blog/free-threaded-one-year-recap

Pyrefly: A new type checker and IDE experience for Python https://engineering.fb.com/2025/05/15/developer-tools/introducing-pyrefly-a-new-type-checker-and-ide-experience-for-python/

agent-api A minimal, open-source setup for serving Agents using FastAPI and Postgres. Built for speed, clarity, and dev happiness. https://github.com/agno-agi/agent-api

Cogitator – A Python Toolkit for Chain-of-Thought Prompting https://github.com/habedi/cogitator

Scaling Python Task Queues Effectively Task queues are a great way to offload work to the background but scaling Python task queues is tricky. Learn how you can keep your task queues chugging along! https://judoscale.com/blog/scaling-python-task-queues

Python Hub Weekly Digest for 2025-05-18 https://pythonhub.dev/digest/2025-05-18/

The REAL Reason You Should Use Type Hints in Python This video explains that the true power of type hints in Python lies in how they encourage better code design by prompting developers to think more generically about data structures. The video also highlights the principle that inputs should be as generic as possible (contravariant), while outputs should be as specific as possible (covariant). https://www.youtube.com/watch?v=0oBLMwHdZ2Y

contextgem Effortless LLM extraction from documents. https://github.com/shcherbak-ai/contextgem

Procrastinate - PostgreSQL Task Queue for Python (Celery Alternative!) This video introduces Procrastinate, a PostgreSQL-based task queue for Python, offering a simpler alternative to Celery by leveraging Postgres for task management, eliminating the need for additional services like Redis or RabbitMQ. The tutorial covers setting up Procrastinate with Django, defining and triggering tasks, including scheduled tasks, and integrating it with the Django admin ... https://www.youtube.com/watch?v=6puV9y7K0z8

pipask Safer python package installs with audit and consent 𝘣𝘦𝘧𝘰𝘳𝘦 install. https://github.com/feynmanix/pipask

SparkDQ A declarative PySpark framework for row- and aggregate-level data quality validation. https://github.com/sparkdq-community/sparkdq

US Routing – Python library for fast local routing in the United States https://github.com/ivanbelenky/us-routing

How SHOULD you install Python on Mac OS? https://www.reddit.com/r/learnpython/comments/1k2bpoo/how_should_you_install_python_on_mac_os/

WebThinker Empowering Large Reasoning Models with Deep Research Capability. https://github.com/RUC-NLPIR/WebThinker

Vibe Coding with Django, React and Cursor: My Experience and Takeaways Cory Zue shares his experience with "vibe coding," a new approach to software development using AI tools, and how he built a Django/React app calleddjobs.devin just two days. He emphasizes the importance of planning, iterating, and reviewing code when working with AI, and shares tips for effectively using tools like Cursor and managing rules files. https://www.youtube.com/watch?v=ELiiMX_o2LI

Polycompiler: Merge Python and JavaScript code into one file that runs in both https://github.com/EvanZhouDev/polycompiler

Asyncio Demystified: Rebuilding it From Scratch One Yield at a Time The article demystifies Python’s asyncio by rebuilding its core concepts from scratch, starting with basic generators and coroutines, then constructing a cooperative multitasking scheduler, and finally integrating non-blocking I/O using custom awaitable Future objects. Through step-by-step code examples, it shows how Python’s async/await syntax is just syntactic sugar over these primitiv... https://dev.indooroutdoor.io/asyncio-demystified-rebuilding-it-from-scratch-one-yield-at-a-time

Function calling using LLMs While LLMs excel at generating cogent text based on their training data, they may also need to interact with external systems. Function calling allows them to construct such calls. The LLM does not execute these calls directly, instead it creates a data structure that describes the call, passing that to a separate program for execution and further processing. The LLM's prompt includes de... https://martinfowler.com/articles/function-call-LLM.html

Suna Open Source Generalist AI Agent. https://github.com/kortix-ai/suna