AI Engineering Hub

Free & Open Source

The path from transformers to production AI

One sequential curriculum — transformers, RAG, agents, harnesses, evals, and LLMOps. No scattered tutorials. Just start at course 01 and build real systems.

Start Here Browse 16 Courses ★ Star on GitHub

16 Courses 140+ Lessons 3 Specialized Tracks 30+ Hands-on Labs MIT License

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Quick start path

1 Start Here Pick a path by background 2 Browse Courses 16 courses, sequential order 3 Study Plans Week-by-week schedule 4 Build Projects Ship a portfolio piece

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🗺️ Curriculum Architecture

flowchart TD
    classDef foundation fill:#eef2ff,stroke:#6366f1,stroke-width:2px,color:#312e81;
    classDef build fill:#f0fdf4,stroke:#10b981,stroke-width:2px,color:#064e3b;
    classDef prod fill:#fff7ed,stroke:#f59e0b,stroke-width:2px,color:#78350f;
    classDef adv fill:#fdf2f8,stroke:#f43f5e,stroke-width:2px,color:#881337;
    classDef track fill:#f5f3ff,stroke:#8b5cf6,stroke-width:2px,color:#4c1d95;

    subgraph Phase1["1. Understand AI (Courses 01–05)"]
        C01["01. GenAI Foundations"]:::foundation --> C02["02. AI Essentials"]:::foundation
        C02 --> C03["03. Neural Networks"]:::foundation
        C03 --> C04["04. Transformers & Attention"]:::foundation
        C04 --> C05["05. Large Language Models"]:::foundation
    end

    subgraph Phase2["2. Build Systems (Courses 06–11)"]
        C05 --> C06["06. RAG Systems"]:::build
        C06 --> C07["07. AI Agents"]:::build
        C07 --> C08["08. Agent Harness & Runtime"]:::build
        C08 --> C09["09. Multi-Agent Systems"]:::build
        C06 --> C10["10. Vector Databases"]:::build
        C07 --> C11["11. Prompt Mastery"]:::build
    end

    subgraph Phase3["3. Production & Scale (Courses 12–14)"]
        C09 --> C12["12. LLMOps & Serving"]:::prod
        C12 --> C13["13. LLM Evals & Quality"]:::prod
        C13 --> C14["14. AI Safety & Guardrails"]:::prod
    end

    subgraph Phase4["4. Advanced & Capstones (Courses 15–16)"]
        C14 --> C15["15. Fine-Tuning & Quantization"]:::adv
        C15 --> C16["16. Enterprise Capstone Projects"]:::adv
    end

    subgraph Tracks["Specialized Role Tracks"]
        T1["Track 1: Agent Engineering"]:::track
        T2["Track 2: Interview Prep & System Design"]:::track
        T3["Track 3: Modern AI & IDE Agents (2026)"]:::track
    end

    C07 -.-> T1
    C12 -.-> T2
    C08 -.-> T3

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🎯 Who is this for?

🌱

New to AI

Software engineer or student starting from ground zero with Python and LLMs.

Start Here →

🧠

Know ML, need LLMs

ML practitioner catching up on modern transformers, APIs, vector DBs, and fine-tuning.

Browse Curriculum →

🤖

Building AI Agents

Engineer shipping autonomous agent loops, tools, MCP, and multi-agent systems.

Agent Track →

⚡

IDE & Coding Agents

Mastering Claude Code, Cursor skills, execution loops, and context engineering.

Modern AI (2026) →

🛡️

Shipping to Production

Architecting LLMOps, automated evals, continuous monitoring, and security guardrails.

Production Track →

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More shortcuts

Topic Map FAQ Setup

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⚡ The Learning Roadmap

Stage	Courses	Core Topics Covered
1. Understand AI	01–05	NLP → neural nets → transformers → attention → LLM architecture
2. Build Applications	06–11	Modular RAG, autonomous agents, tool runtime, multi-agent systems, vector DBs, prompts
3. Production & Ops	12–14	Serving, vLLM/Ollama, LLMOps, automated evals, safety & guardrail gateways
4. Advanced	15–16	Fine-tuning (LoRA/QLoRA), quantization, enterprise capstones

Specialized tracks: Agent Engineering · Interview Prep & System Design · Modern AI (2026)

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🤝 Contribute & Star

If this open handbook helps you learn or ship AI systems, star the repository on GitHub to support open AI education.

Improve a lesson, fix a link, or submit an exercise: Contribute Guide · Roadmap