PythonHub

openai-agents-python A lightweight, powerful framework for multi-agent workflows. https://github.com/openai/openai-agents-python

sourcerer Sourcerer is a CLI-based cloud storage explorer that provides a unified interface for developers and DevOps engineers to view and manage files across multiple cloud providers like GCP Storage, Azure Storage, AWS S3, and S3-compatible services. https://github.com/the-impact-craft/sourcerer

zen-mcp-server The power of Claude Code + Gemini Pro / Flash / O3 / Grok / OpenRouter / Ollama / Custom Model / All Of The Above working as one. https://github.com/BeehiveInnovations/zen-mcp-server

Pixeltable AI Data infrastructure providing a declarative, incremental approach for multimodal workloads. https://github.com/pixeltable/pixeltable

Complete Guide to Build and Deploy an AI Agent with Docker Containers and Python The video is a comprehensive tutorial on building and deploying an AI agent using Python and Docker containers, covering everything from Docker fundamentals to integrating FastAPI, Postgres, LangChain, and LangGraph for multi-agent systems. It walks viewers through local development, containerization, and deployment to platforms like Railway and DigitalOcean, enabling scalable, productio... https://www.youtube.com/watch?v=KC8HT0eWSGk

MCP Explained: How to Expose Your API to AI Models The video explains how to use the Model Context Protocol (MCP) to connect your APIs and external tools with AI language models like ChatGPT or Claude, enabling them to interact with real-world data and services. It covers two main architectural patterns for MCP integration, provides practical Python code examples, and offers tips for building scalable, maintainable MCP servers for AI app... https://www.youtube.com/watch?v=r0QIuI1wpes

llm-memorization Give your local LLM a real memory with a lightweight, fully local memory system — just like a human recalling past discussions. 100% offline. 100% under your control. https://github.com/victorcarre6/llm-memorization

Understanding and Coding the KV Cache in LLMs from Scratch The article explains how KV (Key-Value) caching in large language models (LLMs) speeds up text generation by storing and reusing intermediate computations, significantly improving inference efficiency. It provides a step-by-step, from-scratch code implementation of a KV cache, highlighting both its computational benefits and increased memory requirements during production use. https://magazine.sebastianraschka.com/p/coding-the-kv-cache-in-llms

coleam00 / local-ai-packaged Run all your local AI together in one package - Ollama, Supabase, n8n, Open WebUI, and more! https://github.com/coleam00/local-ai-packaged

This Secret Math Equation let the US Government Spy on Anyone The article provides a hands-on coding guide to the Dual EC DRBG cryptographic backdoor, showing how the NSA-designed algorithm allowed attackers with secret knowledge to predict random outputs and decrypt secure communications. It explains the math behind the backdoor, demonstrates its practical exploitation in Python, and highlights the real-world risks of insecure random number genera... https://leetarxiv.substack.com/p/dual-ec-backdoor-coding-guide

EnrichMCP – A Python ORM for Agents https://github.com/featureform/enrichmcp

MiniMax-AI The world's first open-weight, large-scale hybrid-attention reasoning model. https://github.com/MiniMax-AI/MiniMax-M1

Is uvloop still faster than asyncio's event loop in python3.13? https://www.reddit.com/r/Python/comments/1l8fwu1/is_uvloop_still_faster_than_asyncios_event_loop/

A Python-first data lakehouse https://www.bauplanlabs.com/blog/everything-as-python

miniDiffusion A reimplementation of Stable Diffusion 3.5 in pure PyTorch. https://github.com/yousef-rafat/miniDiffusion

dnsimg - storing images in txt records The author experiments with storing images in DNS TXT records by converting image data to hex, splitting it into 2048-character chunks, and creating a protocol-like method for retrieval and reconstruction. The process demonstrates both the feasibility and practical limitations of this approach, including DNS record size constraints and the need for custom scripts to upload, fetch, and re... https://asherfalcon.com/blog/posts/2

What ever happened to "Zope"?! https://www.reddit.com/r/Python/comments/1l9furl/what_ever_happened_to_zope/

Python can run Mojo now The post explores how Python can now call Mojo code, offering a promising way to speed up Python functions with a simple compiled language. While still early and showing some rough edges like overflow issues, Mojo demonstrates significant performance gains in examples like prime counting, making it an exciting tool for Python developers seeking faster execution. https://koaning.io/posts/giving-mojo-a-spin/

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

ML-GSAI / LLaDA Official PyTorch implementation for "Large Language Diffusion Models" https://github.com/ML-GSAI/LLaDA