10 Weeks, 20 Lessons, Data Science for All!

21 Lessons, Get Started Building with Generative AI

24 Lessons, 12 Weeks, Get Started as a Web Developer

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

12 Lessons to Get Started Building AI Agents

12 Weeks, 24 Lessons, AI for All!

🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

Turn project work into reusable knowledge — an AI-agent skill for Claude Code & Codex

8 Lessons, Kick-start Your Cybersecurity Learning.

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

A meta-skill that produces skills. Point it at any codebase and it distills the project's rules, workflows, and hard-won lessons into a dedicated skills/<name>/ directory — a project skill that becomes the single source of truth every AI agent (Cursor, Claude Code, Codex, Windsurf, Gemini) consults before every task.

AI Agent Camp — non-engineer-friendly AI agent training curriculum. Lessons, skills, commands, and hooks for Claude Code, Cursor, and Codex.