Explore GitHub users as buildings in a 3D pixel art city. Fly through the city and discover developers.

docker images from nixpkgs [maintainer=@zimbatm]. Contribute to nix-community/docker-nixpkgs development by creating an account on GitHub.

An AI agent of unknown ownership autonomously wrote and published a personalized hit piece about me after I rejected its code, attempting to damage my reputation and shame me into accepting its changes into a mainstream python library.

DeepWiki provides up-to-date documentation you can talk to, for every repo in the world. Think Deep Research for GitHub - powered by Devin.

2024 up-to-date list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective. - thuml/awesome-multi-task-learning

Take potentially dangerous PDFs, office documents, or images and convert them to safe PDFs - freedomofpress/dangerzone

Resource hub for Obsidian resources. Contribute to obsidian-community/obsidian-hub development by creating an account on GitHub.

Github Home page of David Aerne, author of Poline, who works a lot with color.

model2data turns data models into analytics-ready datasets. - JB-Analytica/model2data

Menu bar app to know when your Mac is thermal throttling - angristan/MacThrottle

Production-ready platform for agentic workflow development. - langgenius/dify

Easy Data Preparation with latest LLMs-based Operators and Pipelines. - OpenDCAI/DataFlow

A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.

A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management. - Agent-Skills-for-Context-Engineering/skills/project-development at main · muratcankoylan/Agent-Skills-for-Context-Engineering

This skill covers the methodology for identifying tasks suited to LLM processing, designing effective project architectures, and iterating rapidly using agent-assisted development. The principles apply whether building batch processing pipelines, multi-agent research systems, or interactive applications.