# ai-engineering-from-scratch

> Comprehensive open-source AI engineering curriculum and hands-on labs

A 20-phase hands-on AI engineering curriculum covering math foundations, deep learning, LLMs, and agent systems. Learners implement algorithms, models, and protocols from scratch across Python, TypeScript, Rust, and Julia. Lessons include runnable code labs, quizzes, and integrated AI tutor skills for interactive learning.

- Source: https://github.com/rohitg00/ai-engineering-from-scratch
- Homepage: https://aiengineeringfromscratch.com
- Live demo: https://aiengineeringfromscratch.com/
- License: MIT
- Language: Python
- Stars: 63250
- Forks: 10813
- Contributors: 21
- Last commit: 2026-10-02
- Latest release: v2026.10 (2026-09-27)
- Purpose: AI & LLM tooling, MCP servers, Developer tools
- Runs on: Web, CLI
- For: Personal

## Worth score: 79/100

How much DigGitHub recommends it, from activity, adoption, docs, license and security signals.

- Popularity: 24/25
- Momentum: 10/20
- Maintenance: 25/25
- Community: 12/15
- Readiness: 8/15

## Editor note

This is a learning roadmap, not a tooling, will add a new category and reassign

## Alternatives

- [arcadedb](https://diggithub.com/ArcadeData/arcadedb.md): Multi-model DBMS supporting graph, document, vector, and SQL
- [Repomix](https://diggithub.com/yamadashy/repomix.md): Pack a repository into one AI-friendly file

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Source page: https://diggithub.com/rohitg00/ai-engineering-from-scratch
Updated: 2026-10-04