PythonHub

ahujasid / blender-mcp https://github.com/ahujasid/blender-mcp

memU Memory infrastructure for LLMs and AI agents. https://github.com/NevaMind-AI/memU

Engram Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models. https://github.com/deepseek-ai/Engram

ComposioHQ / awesome-claude-skills A curated list of awesome Claude Skills, resources, and tools for customizing Claude AI workflows https://github.com/ComposioHQ/awesome-claude-skills

stocksTUI Real-time stock market data in your terminal. https://github.com/andriy-git/stocksTUI

pythea LLM reliability research from Hassana Labs. https://github.com/leochlon/pythea

Seaborn Crash Course - Data Visualization in Python This video today is a crash course on Seaborn, a data visualization library for Python built on top of Matplotlib. It makes it super simple to create statistical graphs of datasets.<br> https://www.youtube.com/watch?v=rLVCSmtoA7U

BeatBoss BeatBoss is a desktop music player built with Python and Flet. https://github.com/TheVolecitor/BeatBoss

The Emperor Has No Clothes: How to Code Claude Code in 200 Lines of Code Mihail Eric argues that the core functionality of complex AI coding tools like Claude Code or Cursor is not magic but rather simple agentic logic. He demonstrates that these systems can be replicated in about 200 lines of Python by focusing on the essential loop of reading files, editing code, and executing shell commands. https://www.mihaileric.com/The-Emperor-Has-No-Clothes

How I Used Claude to Build a Transcription Bot that Learns From Its Mistakes John Voorhees describes building a transcription pipeline that combines deterministic Python scripts for initial cleanup with Claude's "fuzzy" reasoning to catch obscure phonetic errors. The system uses a feedback loop where Claude identifies misspellings like "goti" for "GOTY" and automatically updates a persistent dictionary to ensure the bot becomes more accurate with every episode. https://www.macstories.net/stories/how-i-used-claude-to-build-a-transcription-bot-that-learns-from-its-mistakes

Python Hub Weekly Digest for 2026-01-18 https://pythonhub.dev/digest/2026-01-18/

pyPFC: An open-source Python package for phase field crystal simulations A Python framework for setting up, running and analyzing 3D Phase Field Crystal (PFC) simulations. https://github.com/HHallb/pyPFC

SnackBase – Open-source, GxP-compliant back end for Python teams https://snackbase.dev

Moving Scratch generation to Python on browser https://kushaldas.in/posts/introducing-ektupy.html

Seapie – a Python debugger where breakpoints drop into a REPL https://github.com/hirsimaki-markus/seapie

Axis – A systems programming language with Python syntax https://github.com/AGDNoob/axis-lang

davila7 / claude-code-templates CLI tool for configuring and monitoring Claude Code https://github.com/davila7/claude-code-templates

Jsonic – Python JSON serialization that works https://medium.com/dev-genius/jsonic-python-serialization-that-just-works-3b38d07c426d

I built a wrapper to get unlimited free access to GPT-4o, Gemini 2.5, and Llama 3 (16k+ reqs/day) https://www.reddit.com/r/Python/comments/1q87ngh/i_built_a_wrapper_to_get_unlimited_free_access_to/

I Made a Classic Refactoring Mistake Arjan addresses mistakes in his prior video's refactoring of a messy order approval function, identifying three logic errors via failing tests. He discusses refactoring pitfalls like incomplete tests (86% coverage despite bugs), tricky and/or conditions, data structures, and the lack of a true "ground truth" in business logic. https://www.youtube.com/watch?v=b6RnBKvQ40o