The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
AI Engineering From Scratch
Learn AI Engineering! Learn it. Build it. Ship it for others.
Install
$
npx skills add rohitg00/ai-engineering-from-scratchClaude Code
claude mcp add ai-engineering-from-scratch -- npx skills add rohitg00/ai-engineering-from-scratchCursor / Claude Desktop (mcp.json)
{
"mcpServers": {
"ai-engineering-from-scratch": {
"command": "npx",
"args": [
"skills",
"add",
"rohitg00/ai-engineering-from-scratch"
]
}
}
}Install command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Implement model internals, retrieval pipelines, and agent runtimes. Test them, inspect failures, and keep the code and evaluation results.
Start learning · Choose a path · Try a lab · Build a project · Browse the curriculum
Free, open source, MIT. Learn on the website, with a coding agent, or by running local code.
542 lessons. 20 phases. Python, TypeScript, Rust, Julia.
Read in your language
🇬🇧 English · 🇨🇳 简体中文 · 🇹🇼 繁體中文（台灣） · 🇯🇵 日本語 · 🇰🇷 한국어 · 🇵🇹 Português · 🇧🇷 Português (Brasil) · 🇪🇸 Español · 🇩🇪 Deutsch · 🇫🇷 Français · 🇮🇹 Italiano · 🇳🇱 Nederlands · 🇵🇱 Polski · 🇨🇿 Čeština · 🇷🇴 Română · 🇭🇺 Magyar · 🇬🇷 Ελληνικά · 🇸🇪 Svenska · 🇩🇰 Dansk · 🇳🇴 Norsk · 🇫🇮 Suomi · 🇷🇺 Русский · 🇺🇦 Українська · 🇹🇷 Türkçe · 🇮🇱 עברית · 🇸🇦 العربية · 🇮🇷 فارسی · 🇮🇳 हिन्दी · 🇧🇩 বাংলা · 🇵🇰 اردو · 🇹🇭 ไทย · 🇻🇳 Tiếng Việt · 🇮🇩 Bahasa Indonesia · 🇵🇭 Tagalog
Sponsors
Your support keeps every lesson free and open source. See all supporters · Become a sponsor
Learning paths
| Route | Starting lesson |
|---|---|
| Model foundations | Setup and tooling |
| LLM systems | Prompt engineering |
| Agents and delivery | The agent loop |
Compare career paths · Prerequisites and study time
Gradient descent
Twenty starting points follow gradient descent on a quadratic loss. The graph shows their positions and mean loss after each update.
Adjust the learning rate in the lesson · Compare GD, momentum, and Adam in code
Projects
Three projects with staged starters, reference implementations, and local graders. Run commands from the repository root after setup. Starters fail until you implement the stages.
01 · Retrieval Evaluation Lab · Python · Ranking metrics and regression checks
A candidate improves mean NDCG while one query ranks its most relevant evidence lower. Build a query-by-query comparison that reports the regression and can fail a release check.
Use Python 3.10+. Review RAG and model evaluation. Implement ranking validation, precision and recall, rank-sensitive metrics, then system comparison.
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