Writing Python Functions Like a Mad Scientist

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...

https://www.youtube.com/watch?v=OdDI-5PBpSk

cognee

Memory for AI Agents in 5 lines of code.

https://github.com/topoteretes/cognee

LMCache

Redis for LLMs - Infinite and Ultra-Fast.

https://github.com/LMCache/LMCache

excel-mcp-server

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.

https://github.com/haris-musa/excel-mcp-server

Gemini API with Python

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.

https://www.youtube.com/watch?v=qfWpPEgea2A

Fixing FastAPI Throughput Without Going Fully Async

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.

https://dpdzero.com/blogs/fixing-fastapi-throughput-without-going-fully-async/

panda-agi

PandaAGI provides a simple, intuitive API for building general AI agents in just a few lines of code.

https://github.com/sinaptik-ai/panda-agi

Optimizing Django Docker Builds with Astral’s

Learn how to speed up and harden your Django Docker builds using Astral’s uv for faster installs, better caching, and reproducible environments.

https://rob.cogit8.org/posts/optimizing-django-docker-builds-with-astrals-uv/

Recent Frontier Models Are Reward Hacking

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 eliminati...

https://metr.org/blog/2025-06-05-recent-reward-hacking/

pyvers

A Python library for dynamic dispatch based on module versions and backends.

https://github.com/vmoens/pyvers

Archon

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.

https://github.com/coleam00/Archon

CRUDAdmin

Modern admin interface for FastAPI with built-in authentication, event tracking, and security features.

https://github.com/benavlabs/crudadmin

bitssh

A New and Modern SSH connector written in Python.

https://github.com/Mr-Sunglasses/bitssh

Premier

A Flexible, Lightweight API-Gateway written in python that can be used as an ASGI middleware, app, or decorators.

https://github.com/raceychan/premier

The fastest way to detect a vowel in a string

The author explores 11 different methods for detecting vowels in a string using Python, benchmarking their performance and analyzing their underlying implementation, including Python bytecode and regex internals. The results show that for short strings, a simple loop is fastest, but for longer strings, regex-based approaches outperform others due to their optimized C-level implementation...

https://austinhenley.com/blog/vowels.html

ML-GSAI / LLaDA

Official PyTorch implementation for "Large Language Diffusion Models"

https://github.com/ML-GSAI/LLaDA

Programming Language Design in the Era of LLMs: A Return to Mediocrity?

The article argues that the rise of LLMs is making it less appealing to design new domain-specific languages (DSLs), since LLMs excel at generating code in popular languages like Python but struggle with niche DSLs. It explores how language designers might adapt by teaching LLMs about DSLs, integrating informal and formal workflows, and focusing on verified specification languages, but w...

https://kirancodes.me/posts/log-lang-design-llms.html