ai-engineering-from-scratch

Learn it. Build it. Ship it for others.

46,557 stars Python Markdown Skills API Spec #agents#ai#ai-agents#ai-engineering
AI Prompts & Specs

<p align="center">
<img src="assets/banner.svg" alt="AI Engineering from Scratch โ€” reference manual banner" width="100%">
</p>

<p align="center">
<b>Read in your language:</b>
<a href="i18n/es/README.md">Espaรฑol</a> ยท
<a href="i18n/fr/README.md">Franรงais</a> ยท
<a href="i18n/pt/README.md">Portuguรชs</a> ยท
<a href="i18n/de/README.md">Deutsch</a> ยท
<a href="i18n/it/README.md">Italiano</a> ยท
<a href="i18n/zh/README.md">็ฎ€ไฝ“ไธญๆ–‡</a> ยท
<a href="i18n/ja/README.md">ๆ—ฅๆœฌ่ชž</a> ยท
<a href="i18n/ko/README.md">ํ•œ๊ตญ์–ด</a> ยท
<a href="i18n/hi/README.md">เคนเคฟเคจเฅเคฆเฅ€</a> ยท
<a href="i18n/ar/README.md">ุงู„ุนุฑุจูŠุฉ</a> ยท
<a href="i18n/ru/README.md">ะ ัƒััะบะธะน</a> ยท
<a href="i18n/tr/README.md">Tรผrkรงe</a>
<br><sub>Translated landing pages, committed to the repo. English is canonical; lesson pages are machine-translated on the <code>translations</code> branch. See <a href="docs/i18n.md">docs/i18n.md</a>.</sub>
</p>

<p align="center">
<a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-1a1a1a?style=flat-square&labelColor=fafaf5" alt="MIT License"></a>
<a href="ROADMAP.md"><img src="https://img.shields.io/badge/lessons-503-3553ff?style=flat-square&labelColor=fafaf5" alt="503 lessons"></a>
<a href="#contents"><img src="https://img.shields.io/badge/phases-20-3553ff?style=flat-square&labelColor=fafaf5" alt="20 phases"></a>
<a href="https://github.com/rohitg00/ai-engineering-from-scratch/stargazers"><img src="https://img.shields.io/github/stars/rohitg00/ai-engineering-from-scratch?style=flat-square&labelColor=fafaf5&color=3553ff" alt="GitHub stars"></a>
<a href="https://aiengineeringfromscratch.com"><img src="https://img.shields.io/badge/web-aiengineeringfromscratch.com-3553ff?style=flat-square&labelColor=fafaf5" alt="Website"></a>
</p>

From the creator of Agent Memory - #1 Persistent memory โญ <a href="https://github.com/rohitg00/agentmemory/stargazers"><img src="https://img.shields.io/github/stars/rohitg00/agentmemory?style=flat-square&labelColor=fafaf5&color=3553ff" alt="GitHub stars"></a> which naturally works with any agents or chat assistants.

text
โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’

84% of students already use AI tools. Only 18% feel prepared to use them

professionally. This curriculum closes that gap.

> 503 lessons. 20 phases. ~320 hours. Python, TypeScript, Rust, Julia. Every lesson ships

a reusable artifact: a prompt, a skill, an agent, an MCP server. Free, open source, MIT.

> You don't just learn AI. You build it. End-to-end. By hand.

<!-- STATS:START (generated from site/stats.json by build.js โ€” do not edit by hand) -->
<p align="center"><sub><b>150,639</b> readers &nbsp;ยท&nbsp; <b>241,669</b> page views in the last 30 days &nbsp;ยท&nbsp; as of 2026-06-07</sub></p>
<!-- STATS:END -->

Start learning in 30 seconds

Your coding agent becomes your tutor. Two commands, no clone, no setup:

bash
npx skills add rohitg00/ai-engineering-from-scratch

Then, inside your agent:

text
/start-learning

A ten-question placement quiz maps what you already know to a starting phase and
saves a personalized study plan to LEARNING.md. From there, /learn teaches
one lesson per session โ€” concept, math, code, quiz โ€” streaming lessons straight
from this repo, and /course-guide <topic> jumps you to the exact lesson that
covers anything you are stuck on.

Works with Claude Code, Cursor, Codex, OpenClaw, Hermes, or any agent that
reads a SKILL.md directory โ€” the installer asks which agents to set up. No
agent? Read the same lessons at
aiengineeringfromscratch.com.

How this works

Most AI material teaches in scattered pieces. A paper here, a fine-tuning post there, a
flashy agent demo somewhere else. The pieces rarely line up. You ship a chatbot but can't
explain its loss curve. You hook a function to an agent but can't say what attention does
inside the model that's calling it.

This curriculum is the spine. 20 phases, 503 lessons, four languages: Python, TypeScript,
Rust, Julia. Linear algebra at one end, autonomous swarms at the other. Every algorithm
gets built from raw math first. Backprop. Tokenizer. Attention. Agent loop. By the time
PyTorch shows up, you already know what it's doing under the hood.

Each lesson runs the same loop: read the problem, derive the math, write the code, run
the test, keep the artifact. No five-minute videos, no copy-paste deploys, no hand-holding.
Free, open source, and built to run on your own laptop.

text
โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’

The shape of the curriculum

Twenty phases stack on top of each other. Math is the floor. Agents and production are the roof.
Skip ahead if you already know the lower layers, but don't skip and then wonder why something at
the top is breaking.

mermaid
%%{init: {'theme':'base','themeVariables':{'primaryColor':'#fafaf5','primaryTextColor':'#1a1a1a','primaryBorderColor':'#3553ff','lineColor':'#3553ff','fontFamily':'JetBrains Mono','fontSize':'12px'}}}%%
flowchart TB
  P0["Phase 0 โ€” Setup &amp; Tooling"] --> P1["Phase 1 โ€” Math Foundations"]
  P1 --> P2["Phase 2 โ€” ML Fundamentals"]
  P2 --> P3["Phase 3 โ€” Deep Learning Core"]
  P3 --> P4["Phase 4 โ€” Vision"]
  P3 --> P5["Phase 5 โ€” NLP"]
  P3 --> P6["Phase 6 โ€” Speech &amp; Audio"]
  P3 --> P9["Phase 9 โ€” RL"]
  P5 --> P7["Phase 7 โ€” Transformers"]
  P7 --> P8["Phase 8 โ€” GenAI"]
  P7 --> P10["Phase 10 โ€” LLMs from Scratch"]
  P10 --> P11["Phase 11 โ€” LLM Engineering"]
  P10 --> P12["Phase 12 โ€” Multimodal"]
  P11 --> P13["Phase 13 โ€” Tools &amp; Protocols"]
  P13 --> P14["Phase 14 โ€” Agent Engineering"]
  P14 --> P15["Phase 15 โ€” Autonomous Systems"]
  P15 --> P16["Phase 16 โ€” Multi-Agent &amp; Swarms"]
  P14 --> P17["Phase 17 โ€” Infrastructure &amp; Production"]
  P15 --> P18["Phase 18 โ€” Ethics &amp; Alignment"]
  P16 --> P19["Phase 19 โ€” Capstone Projects"]
  P17 --> P19
  P18 --> P19

text
โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’

The shape of a lesson

Each lesson lives in its own folder, with the same structure across the entire curriculum:

text
phases/<NN>-<phase-name>/<NN>-<lesson-name>/
โ”œโ”€โ”€ code/      runnable implementations (Python, TypeScript, Rust, Julia)
โ”œโ”€โ”€ docs/
โ”‚   โ””โ”€โ”€ en.md  lesson narrative
โ””โ”€โ”€ outputs/   prompts, skills, agents, or MCP servers this lesson produces

Every lesson follows six beats. The Build It / Use It split is the spine โ€” you implement the
algorithm from scratch first, then run the same thing through the production library. You
understand what the framework is doing because you wrote the smaller version yourself.

mermaid
%%{init: {'theme':'base','themeVariables':{'primaryColor':'#fafaf5','primaryTextColor':'#1a1a1a','primaryBorderColor':'#3553ff','lineColor':'#3553ff','fontFamily':'JetBrains Mono','fontSize':'13px'}}}%%
flowchart LR
  M["MOTTO<br/><sub>one-line core idea</sub>"] --> Pr["PROBLEM<br/><sub>concrete pain</sub>"]
  Pr --> C["CONCEPT<br/><sub>diagrams &amp; intuition</sub>"]
  C --> B["BUILD IT<br/><sub>raw math, no frameworks</sub>"]
  B --> U["USE IT<br/><sub>same thing in PyTorch / sklearn</sub>"]
  U --> S["SHIP IT<br/><sub>prompt ยท skill ยท agent ยท MCP</sub>"]

Getting started

Three ways in. Pick one.

Option A โ€” learn in your terminal (recommended). Install the learning
skills into any agent and let the course drive itself:

bash
npx skills add rohitg00/ai-engineering-from-scratch

text
/start-learning     # interview + placement quiz -> personalized plan in LEARNING.md
/learn              # next lesson, taught interactively: concept -> math -> code -> quiz
/course-guide rag   # "which lessons teach X?" -> exact lessons + links

Lessons stream from this repo as you go โ€” no clone needed. Progress lives in
LEARNING.md in your project, so every session resumes where you left off.

Option B โ€” read. Open any completed lesson on
aiengineeringfromscratch.com or expand a phase under
Contents. No setup, no cloning.

Option C โ€” clone and run.

bash
git clone https://github.com/rohitg00/ai-engineering-from-scratch.git
cd ai-engineering-from-scratch
python phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py

Cloning also auto-loads the learning skills in Claude Code, and gives every
lesson's code to /learn for real execution instead of read-along.

Prerequisites

- You can write code (any language; Python helps).
- You want to understand how AI actually works, not just call APIs.

Prepare for Claude certifications

The Claude Certification Academy is a free,
open-source preparation program for all four official Claude certification tracks:
Associate Foundations, Developer Foundations, Architect Foundations, and Architect
Professional. Each route combines blueprint-mapped lessons, runnable labs, a
diagnostic, capstone work, and a full-length original practice exam.

Use the AI-native GitHub onboarding guide
with Claude Code, Codex, ChatGPT, Cursor, or another agent. Run
/claude-certification to choose a track, create a persistent route in
CLAUDE-CERTIFICATION.md, learn one step at a time, execute the real labs, and
get artifact-based feedback. The same curriculum remains available on the
certification website.

The academy is independent study material based on public exam objectives. It is not
affiliated with Anthropic, does not reproduce live exam questions, and cannot guarantee
a passing score.

The learning skills (any agent: Claude, Cursor, Codex, OpenClaw, Hermes, โ€ฆ)

| Skill | What it does |
|---|---|
| /start-learning | One-time onboarding: why you're learning, placement quiz, personalized plan saved to LEARNING.md. |
| /learn | The tutor loop. Warm-up recall, then the next lesson taught interactively, then its quiz; records progress and a review queue. |
| /course-guide | Topic router. "Where do I learn attention?" or "my loss is NaN" โ†’ the exact lessons, with links. |
| /claude-certification | Certification tutor. Chooses CCAO-F, CCDV-F, CCAR-F, or CCAR-P; teaches each lesson; runs labs; reviews artifacts; administers diagnostics and mocks; saves progress. |
| /find-your-level | Ten-question placement quiz. Maps your knowledge to a starting phase and produces a personalized path with hour estimates. |
| /check-understanding <phase> | Per-phase quiz, eight questions, with feedback and specific lessons to review. |

text
โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’

Read the core curriculum as a book

The 20-phase core curriculum under phases/ compiles into a six-volume book series. EPUB and PDF are built by CI from the same core lesson sources and attached to every GitHub release; the links below always resolve to the newest release. Volume numbers index the series, not versions: each copy carries a dated edition stamp, and older editions stay downloadable from their release.

Certification curricula are intentionally not converted into the books. Their
AI tutor state, runnable labs, interactive figures, diagnostics, and timed mocks
remain first-class on GitHub and the website.

| Vol | Title | Phases | Download |
|-----|-------|--------|----------|
| 1 | Foundations ยท Math, Tooling, and Classical Machine Learning | 00-02 | EPUB ยท PDF |
| 2 | Deep Learning ยท Networks, Vision, and Speech | 03, 04, 06 | EPUB ยท PDF |
| 3 | Language ยท NLP Foundations and the Transformer | 05, 07 | EPUB ยท PDF |
| 4 | Large Language Models ยท Generation, Reinforcement, Pretraining, and Engineering | 08-11 | EPUB ยท PDF |
| 5 | Agents ยท Multimodality, Protocols, Autonomy, and Swarms | 12-16 | EPUB ยท PDF |
| 6 | Production ยท Infrastructure, Safety, and Capstones | 17-19 | EPUB ยท PDF |

The book is the snapshot; this repository is the living edition. Every chapter ends with links back to the lesson's animated figures, quiz, and runnable code. Build locally with python3 scripts/build_book.py (pandoc required); pipeline details in book/README.md.

text
โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’

Every lesson ships something

Other curricula end with "congratulations, you learned X." Each lesson here ends with a
reusable tool you can install or paste into your daily workflow.

<table>
<tr>
<th align="left" width="25%"><img src="site/assets/figures/001-a-prompts.svg" width="96" height="96" alt="FIG_001.A prompts"/><br/><sub>FIG_001 ยท A</sub><br/><b>PROMPTS</b></th>
<th align="left" width="25%"><img src="site/assets/figures/001-b-skills.svg" width="96" height="96" alt="FIG_001.B skills"/><br/><sub>FIG_001 ยท B</sub><br/><b>SKILLS</b></th>
<th align="left" width="25%"><img src="site/assets/figures/001-c-agents.svg" width="96" height="96" alt="FIG_001.C agents"/><br/><sub>FIG_001 ยท C</sub><br/><b>AGENTS</b></th>
<th align="left" width="25%"><img src="site/assets/figures/001-d-mcp-servers.svg" width="96" height="96" alt="FIG_001.D MCP servers"/><br/><sub>FIG_001 ยท D</sub><br/><b>MCP SERVERS</b></th>
</tr>
<tr>
<td valign="top">Paste into any AI assistant for expert-level help on a narrow task.</td>
<td valign="top">Drop into Claude, Cursor, Codex, OpenClaw, Hermes, or any agent that reads <code>SKILL.md</code>.</td>
<td valign="top">Deploy as autonomous workers โ€” you wrote the loop yourself in Phase 14.</td>
<td valign="top">Plug into any MCP-compatible client. Built end-to-end in Phase 13.</td>
</tr>
</table>

Install the lot with python3 scripts/install_skills.py <target>. Real tools, not homework.

By the end of the curriculum, you have a portfolio of 503 artifacts you actually

understand because you built them.

FIG_002 ยท A worked sample

Phase 14, lesson 1: the agent loop. ~120 lines of pure Python, no dependencies.

<table>
<tr>
<td valign="top" width="50%">

code/agent_loop.py &nbsp; <sub><i>build it</i></sub>

python
def run(query, tools):
    history = [user(query)]
    for step in range(MAX_STEPS):
        msg = llm(history)
        if msg.tool_calls:
            for call in msg.tool_calls:
                result = toolscall.name
                history.append(tool_result(call.id, result))
            continue
        return msg.content
    raise StepLimitExceeded

</td>
<td valign="top" width="50%">

outputs/skill-agent-loop.md &nbsp; <sub><i>ship it</i></sub>

markdown
---
name: agent-loop
description: ReAct-style loop for any tool list
phase: 14
lesson: 01
---
Implement a minimal agent loop that...

outputs/prompt-debug-agent.md

markdown
You are an agent debugger. Given the trace
of an agent run, identify the step where
the agent went wrong and explain why...

</td>
</tr>
</table>

text
โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’

<a id="contents"></a>

Contents

Twenty phases. Click any phase to expand its lesson list.

<a id="phase-0"></a>

Phase 0: Setup & Tooling 12 lessons


Get your environment ready for everything that follows.

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Dev Environment | Build | Python |
| 02 | Git & Collaboration | Learn | โ€” |
| 03 | GPU Setup & Cloud | Build | Python |
| 04 | APIs & Keys | Build | Python |
| 05 | Jupyter Notebooks | Build | Python |
| 06 | Python Environments | Build | Shell |
| 07 | Docker for AI | Build | Docker |
| 08 | Editor Setup | Build | โ€” |
| 09 | Data Management | Build | Python |
| 10 | Terminal & Shell | Learn | โ€” |
| 11 | Linux for AI | Learn | โ€” |
| 12 | Debugging & Profiling | Build | Python |

<details id="phase-1">
<summary><b>Phase 1 โ€” Math Foundations</b> &nbsp;<code>22 lessons</code>&nbsp; <em>The intuition behind every AI algorithm, through code.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Linear Algebra Intuition | Learn | Python, Julia |
| 02 | Vectors, Matrices & Operations | Build | Python, Julia |
| 03 | Matrix Transformations & Eigenvalues | Build | Python, Julia |
| 04 | Calculus for ML: Derivatives & Gradients | Learn | Python |
| 05 | Chain Rule & Automatic Differentiation | Build | Python |
| 06 | Probability & Distributions | Learn | Python |
| 07 | Bayes' Theorem & Statistical Thinking | Build | Python |
| 08 | Optimization: Gradient Descent Family | Build | Python |
| 09 | Information Theory: Entropy, KL Divergence | Learn | Python |
| 10 | Dimensionality Reduction: PCA, t-SNE, UMAP | Build | Python |
| 11 | Singular Value Decomposition | Build | Python, Julia |
| 12 | Tensor Operations | Build | Python |
| 13 | Numerical Stability | Build | Python |
| 14 | Norms & Distances | Build | Python |
| 15 | Statistics for ML | Build | Python |
| 16 | Sampling Methods | Build | Python |
| 17 | Linear Systems | Build | Python |
| 18 | Convex Optimization | Build | Python |
| 19 | Complex Numbers for AI | Learn | Python |
| 20 | The Fourier Transform | Build | Python |
| 21 | Graph Theory for ML | Build | Python |
| 22 | Stochastic Processes | Learn | Python |

</details>

<details id="phase-2">
<summary><b>Phase 2 โ€” ML Fundamentals</b> &nbsp;<code>18 lessons</code>&nbsp; <em>Classical ML โ€” still the backbone of most production AI.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | What Is Machine Learning | Learn | Python |
| 02 | Linear Regression from Scratch | Build | Python |
| 03 | Logistic Regression & Classification | Build | Python |
| 04 | Decision Trees & Random Forests | Build | Python |
| 05 | Support Vector Machines | Build | Python |
| 06 | KNN & Distance Metrics | Build | Python |
| 07 | Unsupervised Learning: K-Means, DBSCAN | Build | Python |
| 08 | Feature Engineering & Selection | Build | Python |
| 09 | Model Evaluation: Metrics, Cross-Validation | Build | Python |
| 10 | Bias, Variance & the Learning Curve | Learn | Python |
| 11 | Ensemble Methods: Boosting, Bagging, Stacking | Build | Python |
| 12 | Hyperparameter Tuning | Build | Python |
| 13 | ML Pipelines & Experiment Tracking | Build | Python |
| 14 | Naive Bayes | Build | Python |
| 15 | Time Series Fundamentals | Build | Python |
| 16 | Anomaly Detection | Build | Python |
| 17 | Handling Imbalanced Data | Build | Python |
| 18 | Feature Selection | Build | Python |

</details>

<details id="phase-3">
<summary><b>Phase 3 โ€” Deep Learning Core</b> &nbsp;<code>13 lessons</code>&nbsp; <em>Neural networks from first principles. No frameworks until you build one.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | The Perceptron: Where It All Started | Build | Python |
| 02 | Multi-Layer Networks & Forward Pass | Build | Python |
| 03 | Backpropagation from Scratch | Build | Python |
| 04 | Activation Functions: ReLU, Sigmoid, GELU & Why | Build | Python |
| 05 | Loss Functions: MSE, Cross-Entropy, Contrastive | Build | Python |
| 06 | Optimizers: SGD, Momentum, Adam, AdamW | Build | Python |
| 07 | Regularization: Dropout, Weight Decay, BatchNorm | Build | Python |
| 08 | Weight Initialization & Training Stability | Build | Python |
| 09 | Learning Rate Schedules & Warmup | Build | Python |
| 10 | Build Your Own Mini Framework | Build | Python |
| 11 | Introduction to PyTorch | Build | Python |
| 12 | Introduction to JAX | Build | Python |
| 13 | Debugging Neural Networks | Build | Python |

</details>

<details id="phase-4">
<summary><b>Phase 4 โ€” Computer Vision</b> &nbsp;<code>28 lessons</code>&nbsp; <em>From pixels to understanding โ€” image, video, 3D, VLMs, and world models.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Image Fundamentals: Pixels, Channels, Color Spaces | Learn | Python |
| 02 | Convolutions from Scratch | Build | Python |
| 03 | CNNs: LeNet to ResNet | Build | Python |
| 04 | Image Classification | Build | Python |
| 05 | Transfer Learning & Fine-Tuning | Build | Python |
| 06 | Object Detection โ€” YOLO from Scratch | Build | Python |
| 07 | Semantic Segmentation โ€” U-Net | Build | Python |
| 08 | Instance Segmentation โ€” Mask R-CNN | Build | Python |
| 09 | Image Generation โ€” GANs | Build | Python |
| 10 | Image Generation โ€” Diffusion Models | Build | Python |
| 11 | Stable Diffusion โ€” Architecture & Fine-Tuning | Build | Python |
| 12 | Video Understanding โ€” Temporal Modeling | Build | Python |
| 13 | 3D Vision: Point Clouds, NeRFs | Build | Python |
| 14 | Vision Transformers (ViT) | Build | Python |
| 15 | Real-Time Vision: Edge Deployment | Build | Python |
| 16 | Build a Complete Vision Pipeline | Build | Python |
| 17 | Self-Supervised Vision โ€” SimCLR, DINO, MAE | Build | Python |
| 18 | Open-Vocabulary Vision โ€” CLIP | Build | Python |
| 19 | OCR & Document Understanding | Build | Python |
| 20 | Image Retrieval & Metric Learning | Build | Python |
| 21 | Keypoint Detection & Pose Estimation | Build | Python |
| 22 | 3D Gaussian Splatting from Scratch | Build | Python |
| 23 | Diffusion Transformers & Rectified Flow | Build | Python |
| 24 | SAM 3 & Open-Vocabulary Segmentation | Build | Python |
| 25 | Vision-Language Models (ViT-MLP-LLM) | Build | Python |
| 26 | Monocular Depth & Geometry Estimation | Build | Python |
| 27 | Multi-Object Tracking & Video Memory | Build | Python |
| 28 | World Models & Video Diffusion | Build | Python |

</details>

<details id="phase-5">
<summary><b>Phase 5 โ€” NLP: Foundations to Advanced</b> &nbsp;<code>29 lessons</code>&nbsp; <em>Language is the interface to intelligence.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Text Processing: Tokenization, Stemming, Lemmatization | Build | Python |
| 02 | Bag of Words, TF-IDF & Text Representation | Build | Python |
| 03 | Word Embeddings: Word2Vec from Scratch | Build | Python |
| 04 | GloVe, FastText & Subword Embeddings | Build | Python |
| 05 | Sentiment Analysis | Build | Python |
| 06 | Named Entity Recognition (NER) | Build | Python |
| 07 | POS Tagging & Syntactic Parsing | Build | Python |
| 08 | Text Classification โ€” CNNs & RNNs for Text | Build | Python |
| 09 | Sequence-to-Sequence Models | Build | Python |
| 10 | Attention Mechanism โ€” The Breakthrough | Build | Python |
| 11 | Machine Translation | Build | Python |
| 12 | Text Summarization | Build | Python |
| 13 | Question Answering Systems | Build | Python |
| 14 | Information Retrieval & Search | Build | Python |
| 15 | Topic Modeling: LDA, BERTopic | Build | Python |
| 16 | Text Generation | Build | Python |
| 17 | Chatbots: Rule-Based to Neural | Build | Python |
| 18 | Multilingual NLP | Build | Python |
| 19 | Subword Tokenization: BPE, WordPiece, Unigram, SentencePiece | Learn | Python |
| 20 | Structured Outputs & Constrained Decoding | Build | Python |
| 21 | NLI & Textual Entailment | Learn | Python |
| 22 | Embedding Models Deep Dive | Learn | Python |
| 23 | Chunking Strategies for RAG | Build | Python |
| 24 | Coreference Resolution | Learn | Python |
| 25 | Entity Linking & Disambiguation | Build | Python |
| 26 | Relation Extraction & Knowledge Graph Construction | Build | Python |
| 27 | LLM Evaluation: RAGAS, DeepEval, G-Eval | Build | Python |
| 28 | Long-Context Evaluation: NIAH, RULER, LongBench, MRCR | Learn | Python |
| 29 | Dialogue State Tracking | Build | Python |

</details>

<details id="phase-6">
<summary><b>Phase 6 โ€” Speech & Audio</b> &nbsp;<code>17 lessons</code>&nbsp; <em>Hear, understand, speak.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Audio Fundamentals: Waveforms, Sampling, FFT | Learn | Python |
| 02 | Spectrograms, Mel Scale & Audio Features | Build | Python |
| 03 | Audio Classification | Build | Python |
| 04 | Speech Recognition (ASR) | Build | Python |
| 05 | Whisper: Architecture & Fine-Tuning | Build | Python |
| 06 | Speaker Recognition & Verification | Build | Python |
| 07 | Text-to-Speech (TTS) | Build | Python |
| 08 | Voice Cloning & Voice Conversion | Build | Python |
| 09 | Music Generation | Build | Python |
| 10 | Audio-Language Models | Build | Python |
| 11 | Real-Time Audio Processing | Build | Python |
| 12 | Build a Voice Assistant Pipeline | Build | Python |
| 13 | Neural Audio Codecs โ€” EnCodec, SNAC, Mimi, DAC | Learn | Python |
| 14 | Voice Activity Detection & Turn-Taking | Build | Python |
| 15 | Streaming Speech-to-Speech โ€” Moshi, Hibiki | Learn | Python |
| 16 | Voice Anti-Spoofing & Audio Watermarking | Build | Python |
| 17 | Audio Evaluation โ€” WER, MOS, MMAU, Leaderboards | Learn | Python |

</details>

<details id="phase-7">
<summary><b>Phase 7 โ€” Transformers Deep Dive</b> &nbsp;<code>16 lessons</code>&nbsp; <em>The architecture that changed everything.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Why Transformers: The Problems with RNNs | Learn | Python |
| 02 | Self-Attention from Scratch | Build | Python |
| 03 | Multi-Head Attention | Build | Python |
| 04 | Positional Encoding: Sinusoidal, RoPE, ALiBi | Build | Python |
| 05 | The Full Transformer: Encoder + Decoder | Build | Python |
| 06 | BERT โ€” Masked Language Modeling | Build | Python |
| 07 | GPT โ€” Causal Language Modeling | Build | Python |
| 08 | T5, BART โ€” Encoder-Decoder Models | Learn | Python |
| 09 | Vision Transformers (ViT) | Build | Python |
| 10 | Audio Transformers โ€” Whisper Architecture | Learn | Python |
| 11 | Mixture of Experts (MoE) | Build | Python |
| 12 | KV Cache, Flash Attention & Inference Optimization | Build | Python |
| 13 | Scaling Laws | Learn | Python |
| 14 | Build a Transformer from Scratch | Build | Python |
| 15 | Attention Variants โ€” Sliding Window, Sparse, Differential | Build | Python |
| 16 | Speculative Decoding โ€” Draft, Verify, Repeat | Build | Python |

</details>

<details id="phase-8">
<summary><b>Phase 8 โ€” Generative AI</b> &nbsp;<code>15 lessons</code>&nbsp; <em>Create images, video, audio, 3D, and more.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Generative Models: Taxonomy & History | Learn | Python |
| 02 | Autoencoders & VAE | Build | Python |
| 03 | GANs: Generator vs Discriminator | Build | Python |
| 04 | Conditional GANs & Pix2Pix | Build | Python |
| 05 | StyleGAN | Build | Python |
| 06 | Diffusion Models โ€” DDPM from Scratch | Build | Python |
| 07 | Latent Diffusion & Stable Diffusion | Build | Python |
| 08 | ControlNet, LoRA & Conditioning | Build | Python |
| 09 | Inpainting, Outpainting & Editing | Build | Python |
| 10 | Video Generation | Build | Python |
| 11 | Audio Generation | Build | Python |
| 12 | 3D Generation | Build | Python |
| 13 | Flow Matching & Rectified Flows | Build | Python |
| 14 | Evaluation: FID, CLIP Score | Build | Python |
| 19 | Visual Autoregressive Modeling (VAR): Next-Scale Prediction | Build | Python |

</details>

<details id="phase-9">
<summary><b>Phase 9 โ€” Reinforcement Learning</b> &nbsp;<code>12 lessons</code>&nbsp; <em>The foundation of RLHF and game-playing AI.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | MDPs, States, Actions & Rewards | Learn | Python |
| 02 | Dynamic Programming | Build | Python |
| 03 | Monte Carlo Methods | Build | Python |
| 04 | Q-Learning, SARSA | Build | Python |
| 05 | Deep Q-Networks (DQN) | Build | Python |
| 06 | Policy Gradients โ€” REINFORCE | Build | Python |
| 07 | Actor-Critic โ€” A2C, A3C | Build | Python |
| 08 | PPO | Build | Python |
| 09 | Reward Modeling & RLHF | Build | Python |
| 10 | Multi-Agent RL | Build | Python |
| 11 | Sim-to-Real Transfer | Build | Python |
| 12 | RL for Games | Build | Python |

</details>

<details id="phase-10">
<summary><b>Phase 10 โ€” LLMs from Scratch</b> &nbsp;<code>24 lessons</code>&nbsp; <em>Build, train, and understand large language models.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Tokenizers: BPE, WordPiece, SentencePiece | Build | Python, Rust |
| 02 | Building a Tokenizer from Scratch | Build | Python |
| 03 | Data Pipelines for Pre-Training | Build | Python |
| 04 | Pre-Training a Mini GPT (124M) | Build | Python |
| 05 | Distributed Training, FSDP, DeepSpeed | Build | Python |
| 06 | Instruction Tuning โ€” SFT | Build | Python |
| 07 | RLHF โ€” Reward Model + PPO | Build | Python |
| 08 | DPO โ€” Direct Preference Optimization | Build | Python |
| 09 | Constitutional AI & Self-Improvement | Build | Python |
| 10 | Evaluation โ€” Benchmarks, Evals | Build | Python |
| 11 | Quantization: INT8, GPTQ, AWQ, GGUF | Build | Python |
| 12 | Inference Optimization | Build | Python |
| 13 | Building a Complete LLM Pipeline | Build | Python |
| 14 | Open Models: Architecture Walkthroughs | Learn | Python |
| 15 | Speculative Decoding and EAGLE-3 | Build | Python |
| 16 | Differential Attention (V2) | Build | Python |
| 17 | Native Sparse Attention (DeepSeek NSA) | Build | Python |
| 18 | Multi-Token Prediction (MTP) | Build | Python |
| 19 | DualPipe Parallelism | Learn | Python |
| 20 | DeepSeek-V3 Architecture Walkthrough | Learn | Python |
| 21 | Jamba โ€” Hybrid SSM-Transformer | Learn | Python |
| 22 | Async and Hogwild! Inference | Build | Python |
| 25 | Speculative Decoding and EAGLE | Build | Python |
| 34 | Gradient Checkpointing and Activation Recomputation | Build | Python |

</details>

<details id="phase-11">
<summary><b>Phase 11 โ€” LLM Engineering</b> &nbsp;<code>17 lessons</code>&nbsp; <em>Put LLMs to work in production.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Prompt Engineering: Techniques & Patterns | Build | Python |
| 02 | Few-Shot, CoT, Tree-of-Thought | Build | Python |
| 03 | Structured Outputs | Build | Python |
| 04 | Embeddings & Vector Representations | Build | Python |
| 05 | Context Engineering | Build | Python |
| 06 | RAG: Retrieval-Augmented Generation | Build | Python |
| 07 | Advanced RAG: Chunking, Reranking | Build | Python |
| 08 | Fine-Tuning with LoRA & QLoRA | Build | Python |
| 09 | Function Calling & Tool Use | Build | Python |
| 10 | Evaluation & Testing | Build | Python |
| 11 | Caching, Rate Limiting & Cost | Build | Python |
| 12 | Guardrails & Safety | Build | Python |
| 13 | Building a Production LLM App | Build | Python |
| 14 | Model Context Protocol (MCP) | Build | Python |
| 15 | Prompt Caching & Context Caching | Build | Python |
| 16 | Agent State Machines โ€” Graphs, Nodes, Checkpoints | Build | Python |
| 17 | Agent Framework Tradeoffs | Learn | Python |

</details>

<details id="phase-12">
<summary><b>Phase 12 โ€” Multimodal AI</b> &nbsp;<code>25 lessons</code>&nbsp; <em>See, hear, read, and reason across modalities โ€” from ViT patches to computer-use agents.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Vision Transformers and the Patch-Token Primitive | Learn | Python |
| 02 | CLIP and Contrastive Vision-Language Pretraining | Build | Python |
| 03 | BLIP-2 Q-Former as Modality Bridge | Build | Python |
| 04 | Flamingo and Gated Cross-Attention | Learn | Python |
| 05 | LLaVA and Visual Instruction Tuning | Build | Python |
| 06 | Any-Resolution Vision โ€” Patch-n'-Pack and NaFlex | Build | Python |
| 07 | Open-Weight VLM Recipes: What Actually Matters | Learn | Python |
| 08 | LLaVA-OneVision: Single, Multi, Video | Build | Python |
| 09 | Qwen-VL Family and Dynamic-FPS Video | Learn | Python |
| 10 | InternVL3 Native Multimodal Pretraining | Learn | Python |
| 11 | Chameleon Early-Fusion Token-Only | Build | Python |
| 12 | Emu3 Next-Token Prediction for Generation | Learn | Python |
| 13 | Transfusion Autoregressive + Diffusion | Build | Python |
| 14 | Show-o Discrete-Diffusion Unified | Learn | Python |
| 15 | Janus-Pro Decoupled Encoders | Build | Python |
| 16 | MIO Any-to-Any Streaming | Learn | Python |
| 17 | Video-Language Temporal Grounding | Build | Python |
| 18 | Long-Video at Million-Token Context | Build | Python |
| 19 | Audio-Language Models: Whisper to AF3 | Build | Python |
| 20 | Omni Models: Thinker-Talker Streaming | Build | Python |
| 21 | Embodied VLAs: RT-2, OpenVLA, ฯ€0, GR00T | Learn | Python |
| 22 | Document and Diagram Understanding | Build | Python |
| 23 | ColPali Vision-Native Document RAG | Build | Python |
| 24 | Multimodal RAG and Cross-Modal Retrieval | Build | Python |
| 25 | Multimodal Agents and Computer-Use (Capstone) | Build | Python |

</details>

<details id="phase-13">
<summary><b>Phase 13 โ€” Tools & Protocols</b> &nbsp;<code>23 lessons</code>&nbsp; <em>The interfaces between AI and the real world.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | The Tool Interface | Learn | Python |
| 02 | Function Calling Deep Dive | Build | Python |
| 03 | Parallel and Streaming Tool Calls | Build | Python |
| 04 | Structured Output | Build | Python |
| 05 | Tool Schema Design | Learn | Python |
| 06 | MCP Fundamentals | Learn | Python |
| 07 | Building an MCP Server | Build | Python |
| 08 | Building an MCP Client | Build | Python |
| 09 | MCP Transports | Learn | Python |
| 10 | MCP Resources and Prompts | Build | Python |
| 11 | MCP Sampling | Build | Python |
| 12 | MCP Roots and Elicitation | Build | Python |
| 13 | MCP Async Tasks | Build | Python |
| 14 | MCP Apps | Build | Python |
| 15 | MCP Security I โ€” Tool Poisoning | Learn | Python |
| 16 | MCP Security II โ€” OAuth 2.1 | Build | Python |
| 17 | MCP Gateways and Registries | Learn | Python |
| 18 | MCP Auth in Production โ€” Enrollment, JWKS Refresh, Audience Pinning | Build | Python |
| 19 | A2A Protocol | Build | Python |
| 20 | OpenTelemetry GenAI | Build | Python |
| 21 | LLM Routing Layer | Learn | Python |
| 22 | Skills and Agent SDKs | Learn | Python |
| 23 | Capstone โ€” Tool Ecosystem | Build | Python |

</details>

<details id="phase-14">
<summary><b>Phase 14 โ€” Agent Engineering</b> &nbsp;<code>42 lessons</code>&nbsp; <em>Build agents from first principles โ€” loop, memory, planning, frameworks, benchmarks, production, workbench.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | The Agent Loop | Build | Python |
| 02 | ReWOO and Plan-and-Execute | Build | Python |
| 03 | Reflexion and Verbal Reinforcement Learning | Build | Python |
| 04 | Tree of Thoughts and LATS | Build | Python |
| 05 | Self-Refine and CRITIC | Build | Python |
| 06 | Tool Use and Function Calling | Build | Python |
| 07 | Agent Memory โ€” Virtual Context and Memory Paging | Build | Python |
| 08 | Memory Blocks and Sleep-Time Compute | Build | Python |
| 09 | Hybrid Memory โ€” Vector + Graph + KV | Build | Python |
| 10 | Skill Libraries and Lifelong Learning (Voyager) | Build | Python |
| 11 | Planning with HTN and Evolutionary Search | Build | Python |
| 12 | Anthropic's Workflow Patterns | Build | Python |
| 13 | Stateful Graph Orchestration โ€” Durable Execution and Checkpoints | Build | Python |
| 14 | The Actor Model for Agents | Build | Python |
| 15 | Role-Based Agent Teams โ€” Roles, Tasks, Processes | Build | Python |
| 16 | OpenAI Agents SDK โ€” Handoffs, Guardrails, Tracing | Build | Python |
| 17 | The Harness as a Library โ€” Subagents and Session Store | Build | Python |
| 18 | Production Agent Runtimes | Learn | Python |
| 19 | Benchmarks โ€” SWE-bench, GAIA, AgentBench | Learn | Python |
| 20 | Benchmarks โ€” WebArena and OSWorld | Learn | Python |
| 21 | Computer Use โ€” Claude, OpenAI CUA, Gemini | Build | Python |
| 22 | Voice Agents โ€” Pipecat and LiveKit | Build | Python |
| 23 | OpenTelemetry GenAI Semantic Conventions | Build | Python |
| 24 | Agent Observability โ€” Langfuse, Phoenix, Opik | Learn | Python |
| 25 | Multi-Agent Debate and Collaboration | Build | Python |
| 26 | Failure Modes โ€” Why Agents Break | Build | Python |
| 27 | Prompt Injection and the PVE Defense | Build | Python |
| 28 | Orchestration Patterns โ€” Supervisor, Swarm, Hierarchical | Build | Python |
| 29 | Production Runtimes โ€” Queue, Event, Cron | Learn | Python |
| 30 | Eval-Driven Agent Development | Build | Python |
| 31 | Agent Workbench: Why Capable Models Still Fail | Learn | Python |
| 32 | The Minimal Agent Workbench | Build | Python |
| 33 | Agent Instructions as Executable Constraints | Build | Python |
| 34 | Repo Memory and Durable State | Build | Python |
| 35 | Initialization Scripts for Agents | Build | Python |
| 36 | Scope Contracts and Task Boundaries | Build | Python |
| 37 | Runtime Feedback Loops | Build | Python |
| 38 | Verification Gates | Build | Python |
| 39 | Reviewer Agent: Separate Builder from Marker | Build | Python |
| 40 | Multi-Session Handoff | Build | Python |
| 41 | The Workbench on a Real Repo | Build | Python |
| 42 | Capstone: Ship a Reusable Agent Workbench Pack | Build | Python |

Each Phase 14 workbench lesson (31-42) ships a mission.md briefing the agent before it opens the full lesson docs.

</details>

<details id="phase-15">
<summary><b>Phase 15 โ€” Autonomous Systems</b> &nbsp;<code>22 lessons</code>&nbsp; <em>Long-horizon agents, self-improvement, and the 2026 safety stack.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | From Chatbots to Long-Horizon Agents (METR) | Learn | Python |
| 02 | STaR, V-STaR, Quiet-STaR: Self-Taught Reasoning | Learn | Python |
| 03 | AlphaEvolve: Evolutionary Coding Agents | Learn | Python |
| 04 | Darwin Gรถdel Machine: Self-Modifying Agents | Learn | Python |
| 05 | AI Scientist v2: Workshop-Level Research | Learn | Python |
| 06 | Automated Alignment Research (Anthropic AAR) | Learn | Python |
| 07 | Recursive Self-Improvement: Capability vs Alignment | Learn | Python |
| 08 | Bounded Self-Improvement Designs | Learn | Python |
| 09 | Autonomous Coding Agent Landscape (SWE-bench, CodeAct) | Learn | Python |
| 10 | Permission Modes for Autonomous Agents | Learn | Python |
| 11 | Browser Agents and Indirect Prompt Injection | Learn | Python |
| 12 | Durable Execution for Long-Running Agents | Learn | Python |
| 13 | Action Budgets, Iteration Caps, Cost Governors | Learn | Python |
| 14 | Kill Switches, Circuit Breakers, Canary Tokens | Learn | Python |
| 15 | HITL: Propose-Then-Commit | Learn | Python |
| 16 | Checkpoints and Rollback | Learn | Python |
| 17 | Constitutional AI and Rule Overrides | Learn | Python |
| 18 | Llama Guard and Input/Output Classification | Learn | Python |
| 19 | Anthropic Responsible Scaling Policy v3.0 | Learn | Python |
| 20 | OpenAI Preparedness Framework and DeepMind FSF | Learn | Python |
| 21 | METR Time Horizons and External Evaluation | Learn | Python |
| 22 | CAIS, CAISI, and Societal-Scale Risk | Learn | Python |

</details>

<details id="phase-16">
<summary><b>Phase 16 โ€” Multi-Agent & Swarms</b> &nbsp;<code>25 lessons</code>&nbsp; <em>Coordination, emergence, and collective intelligence.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Why Multi-Agent | Learn | TypeScript |
| 02 | FIPA-ACL Heritage and Speech Acts | Learn | Python |
| 03 | Communication Protocols | Build | TypeScript |
| 04 | The Multi-Agent Primitive Model | Learn | Python |
| 05 | Supervisor / Orchestrator-Worker Pattern | Build | Python |
| 06 | Hierarchical Architecture and Decomposition Drift | Learn | Python |
| 07 | Society of Mind and Multi-Agent Debate | Build | Python |
| 08 | Role Specialization โ€” Planner / Critic / Executor / Verifier | Build | Python |
| 09 | Parallel Swarm and Networked Architectures | Build | Python |
| 10 | Group Chat and Speaker Selection | Build | Python |
| 11 | Handoffs and Routines (Stateless Orchestration) | Build | Python |
| 12 | A2A โ€” The Agent-to-Agent Protocol | Build | Python |
| 13 | Shared Memory and Blackboard Patterns | Build | Python |
| 14 | Consensus and Byzantine Fault Tolerance | Build | Python |
| 15 | Voting, Self-Consistency, and Debate Topology | Build | Python |
| 16 | Negotiation and Bargaining | Build | Python |
| 17 | Generative Agents and Emergent Simulation | Build | Python |
| 18 | Theory of Mind and Emergent Coordination | Build | Python |
| 19 | Swarm Optimization (PSO, ACO) | Build | Python |
| 20 | MARL โ€” MADDPG, QMIX, MAPPO | Learn | Python |
| 21 | Agent Economies, Token Incentives, Reputation | Learn | Python |
| 22 | Production Scaling โ€” Queues, Checkpoints, Durability | Build | Python |
| 23 | Failure Modes โ€” MAST, Groupthink, Monoculture | Learn | Python |
| 24 | Evaluation and Coordination Benchmarks | Learn | Python |
| 25 | Case Studies and 2026 State of the Art | Learn | Python |

</details>

<details id="phase-17">
<summary><b>Phase 17 โ€” Infrastructure & Production</b> &nbsp;<code>28 lessons</code>&nbsp; <em>Ship AI to the real world.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Managed LLM Platforms โ€” Bedrock, Azure OpenAI, Vertex AI | Learn | Python |
| 02 | Inference Platform Economics โ€” Fireworks, Together, Baseten, Modal | Learn | Python |
| 03 | GPU Autoscaling on Kubernetes โ€” Karpenter, KAI Scheduler | Learn | Python |
| 04 | Serving Engine Internals โ€” PagedAttention, Continuous Batching, Chunked Prefill | Learn | Python |
| 05 | EAGLE-3 Speculative Decoding in Production | Learn | Python |
| 06 | Prefix-Cache Serving โ€” RadixAttention and KV Reuse | Learn | Python |
| 07 | Hardware-Specialized Inference Compilation โ€” FP8 and NVFP4 on Blackwell | Learn | Python |
| 08 | Inference Metrics โ€” TTFT, TPOT, ITL, Goodput, P99 | Learn | Python |
| 09 | Production Quantization โ€” AWQ, GPTQ, GGUF, FP8, NVFP4 | Learn | Python |
| 10 | Cold Start Mitigation for Serverless LLMs | Learn | Python |
| 11 | Multi-Region LLM Serving and KV Cache Locality | Learn | Python |
| 12 | Edge Inference โ€” ANE, Hexagon, WebGPU, Jetson | Learn | Python |
| 13 | LLM Observability Stack Selection | Learn | Python |
| 14 | Prompt Caching and Semantic Caching Economics | Learn | Python |
| 15 | Batch APIs โ€” the 50% Discount as Industry Standard | Learn | Python |
| 16 | Model Routing as a Cost-Reduction Primitive | Learn | Python |
| 17 | Disaggregated Prefill/Decode โ€” NVIDIA Dynamo and llm-d | Learn | Python |
| 18 | Production Serving Stack โ€” KV Offloading and Cache-Aware Routing | Learn | Python |
| 19 | AI Gateways โ€” LiteLLM, Portkey, Kong, Bifrost | Learn | Python |
| 20 | Shadow, Canary, and Progressive Deployment | Learn | Python |
| 21 | A/B Testing LLM Features โ€” GrowthBook and Statsig | Learn | Python |
| 22 | Load Testing LLM APIs โ€” k6, LLMPerf, GenAI-Perf | Build | Python |
| 23 | SRE for AI โ€” Multi-Agent Incident Response | Learn | Python |
| 24 | Chaos Engineering for LLM Production | Learn | Python |
| 25 | Security โ€” Secrets, PII Scrubbing, Audit Logs | Learn | Python |
| 26 | Compliance โ€” SOC 2, HIPAA, GDPR, EU AI Act, ISO 42001 | Learn | Python |
| 27 | FinOps for LLMs โ€” Unit Economics and Multi-Tenant Attribution | Learn | Python |
| 28 | Self-Hosted Serving Selection โ€” Matching Engine to Hardware and Scale | Learn | Python |

</details>

<details id="phase-18">
<summary><b>Phase 18 โ€” Ethics, Safety & Alignment</b> &nbsp;<code>30 lessons</code>&nbsp; <em>Build AI that helps humanity. Not optional.</em></summary>
<br/>

| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | Instruction-Following as Alignment Signal | Learn | Python |
| 02 | Reward Hacking & Goodhart's Law | Learn | Python |
| 03 | Direct Preference Optimization Family | Learn | Python |
| 04 | Sycophancy as RLHF Amplification | Learn | Python |
| 05 | Constitutional AI & RLAIF | Learn | Python |
| 06 | Mesa-Optimization & Deceptive Alignment | Learn | Python |
| 07 | Sleeper Agents โ€” Persistent Deception | Learn | Python |
| 08 | In-Context Scheming in Frontier Models | Learn | Python |
| 09 | Alignment Faking | Learn | Python |
| 10 | AI Control โ€” Safety Despite Subversion | Learn | Python |
| 11 | Scalable Oversight & Weak-to-Strong | Learn | Python |
| 12 | Red-Teaming: PAIR & Automated Attacks | Build | Python |
| 13 | Many-Shot Jailbreaking | Learn | Python |
| 14 | ASCII Art & Visual Jailbreaks | Build | Python |
| 15 | Indirect Prompt Injection | Build | Python |
| 16 | Red-Team Tooling: Garak, Llama Guard, PyRIT | Build | Python |
| 17 | WMDP & Dual-Use Capability Evaluation | Learn | Python |
| 18 | Frontier Safety Frameworks โ€” RSP, PF, FSF | Learn | Python |
| 19 | Model Welfare Research | Learn | Python |
| 20 | Bias & Representational Harm | Build | Python |
| 21 | Fairness Criteria: Group, Individual, Counterfactual | Learn | Python |
| 22 | Differential Privacy for LLMs | Build | Python |
| 23 | Watermarking: SynthID, Stable Signature, C2PA | Build | Python |
| 24 | Regulatory Frameworks: EU, US, UK, Korea | Learn | Python |
| 25 | EchoLeak & CVEs for AI | Learn | Python |
| 26 | Model, System & Dataset Cards | Build | Python |
| 27 | Data Provenance & Training-Data Governance | Learn | Python |
| 28 | Alignment Research Ecosystem: MATS, Redwood, Apollo, METR | Learn | Python |
| 29 | Moderation Systems: OpenAI, Perspective, Llama Guard | Build | Python |
| 30 | Dual-Use Risk: Cyber, Bio, Chem, Nuclear | Learn | Python |

</details>

<details id="phase-19">
<summary><b>Phase 19 โ€” Capstone Projects</b> &nbsp;<code>85 lessons</code>&nbsp; <em>17 end-to-end products + 9 deep-build tracks. 20-40 hours per project; 4-12 lessons per track.</em></summary>
<br/>

| # | Project | Combines | Lang |
|:---:|---------|----------|------|
| 01 | Terminal-Native Coding Agent | P0 P5 P7 P10 P11 P13 P14 P15 P17 P18 | Python |
| 02 | RAG over Codebase (Cross-Repo Semantic Search) | P5 P7 P11 P13 P17 | Python |
| 03 | Real-Time Voice Assistant (ASR โ†’ LLM โ†’ TTS) | P6 P7 P11 P13 P14 P17 | Python |
| 04 | Multimodal Document QA (Vision-First) | P4 P5 P7 P11 P12 P17 | Python |
| 05 | Autonomous Research Agent (AI-Scientist Class) | P0 P2 P3 P7 P10 P14 P15 P16 P18 | Python |
| 06 | DevOps Troubleshooting Agent for Kubernetes | P11 P13 P14 P15 P17 P18 | Python |
| 07 | End-to-End Fine-Tuning Pipeline | P2 P3 P7 P10 P11 P17 P18 | Python |
| 08 | Production RAG Chatbot (Regulated Vertical) | P5 P7 P11 P12 P17 P18 | Python |
| 09 | Code Migration Agent (Repo-Level Upgrade) | P5 P7 P11 P13 P14 P15 P17 | Python |
| 10 | Multi-Agent Software Engineering Team | P11 P13 P14 P15 P16 P17 | Python |
| 11 | LLM Observability & Eval Dashboard | P11 P13 P17 P18 | Python |
| 12 | Video Understanding Pipeline (Scene โ†’ QA) | P4 P6 P7 P11 P12 P17 | Python |
| 13 | MCP Server with Registry and Governance | P11 P13 P14 P17 P18 | Python |
| 14 | Speculative-Decoding Inference Server | P3 P7 P10 P17 | Python |
| 15 | Constitutional Safety Harness + Red-Team Range | P10 P11 P13 P14 P18 | Python |
| 16 | GitHub Issue-to-PR Autonomous Agent | P11 P13 P14 P15 P17 | Python |
| 17 | Personal AI Tutor (Adaptive, Multimodal) | P5 P6 P11 P12 P14 P17 P18 | Python |

Deep-build tracks โ€” multi-lesson series that build a complete subsystem from scratch.

| # | Project | Combines | Lang |
|:---:|---------|----------|------|
| 20 | Agent Harness Loop Contract | A. Agent harness | Python |
| 21 | Tool Registry with Schema Validation | A. Agent harness | Python |
| 22 | JSON-RPC 2.0 Over Newline-Delimited Stdio | A. Agent harness | Python |
| 23 | Function Call Dispatcher | A. Agent harness | Python |
| 24 | Plan-Execute Control Flow | A. Agent harness | Python |
| 25 | Verification Gates and Observation Budget | A. Agent harness | Python |
| 26 | Sandbox Runner with Denylist and Path Jail | A. Agent harness | Python |
| 27 | Eval Harness with Fixture Tasks | A. Agent harness | Python |
| 28 | Observability with OTel GenAI Spans and Prometheus Metrics | A. Agent harness | Python |
| 29 | End-to-End Coding Agent on the Harness | A. Agent harness | Python |
| 30 | BPE Tokenizer From Scratch | B. NLP LLM | Python |
| 31 | Tokenized Dataset with Sliding Window | B. NLP LLM | Python |
| 32 | Token and Positional Embeddings | B. NLP LLM | Python |
| 33 | Multi-Head Self-Attention | B. NLP LLM | Python |
| 34 | Transformer Block from Scratch | B. NLP LLM | Python |
| 35 | GPT Model Assembly | B. NLP LLM | Python |
| 36 | Training Loop and Evaluation | B. NLP LLM | Python |
| 37 | Loading Pretrained Weights | B. NLP LLM | Python |
| 38 | Classifier Fine-Tuning by Head Swap | B. NLP LLM | Python |
| 39 | Instruction Tuning by Supervised Fine-Tuning | B. NLP LLM | Python |
| 40 | Direct Preference Optimization from Scratch | B. NLP LLM | Python |
| 41 | Full Evaluation Pipeline | B. NLP LLM | Python |
| 42 | Large Corpus Downloader | C. Train end-to-end | Python |
| 43 | HDF5 Tokenized Corpus | C. Train end-to-end | Python |
| 44 | Cosine LR with Linear Warmup | C. Train end-to-end | Python |
| 45 | Gradient Clipping and Mixed Precision | C. Train end-to-end | Python |
| 46 | Gradient Accumulation | C. Train end-to-end | Python |
| 47 | Checkpoint Save and Resume | C. Train end-to-end | Python |
| 48 | Distributed Data Parallel and FSDP from Scratch | C. Train end-to-end | Python |
| 49 | Language Model Evaluation Harness | C. Train end-to-end | Python |
| 50 | Hypothesis Generator | D. Auto research | Python |
| 51 | Literature Retrieval | D. Auto research | Python |
| 52 | Experiment Runner | D. Auto research | Python |
| 53 | Result Evaluator | D. Auto research | Python |
| 54 | Paper Writer | D. Auto research | Python |
| 55 | Critic Loop | D. Auto research | Python |
| 56 | Iteration Scheduler | D. Auto research | Python |
| 57 | End-to-End Research Demo | D. Auto research | Python |
| 58 | Vision Encoder Patches | E. Multimodal VLM | Python |
| 59 | Vision Transformer Encoder | E. Multimodal VLM | Python |
| 60 | Projection Layer for Modality Alignment | E. Multimodal VLM | Python |
| 61 | Cross-Attention Fusion | E. Multimodal VLM | Python |
| 62 | Vision-Language Pretraining | E. Multimodal VLM | Python |
| 63 | Multimodal Evaluation | E. Multimodal VLM | Python |
| 64 | Chunking Strategies, Compared | F. Advanced RAG | Python |
| 65 | Hybrid Retrieval with BM25 and Dense Embeddings | F. Advanced RAG | Python |
| 66 | Cross-Encoder Reranker | F. Advanced RAG | Python |
| 67 | Query Rewriting: HyDE, Multi-Query, and Decomposition | F. Advanced RAG | Python |
| 68 | RAG Evaluation: Precision, Recall, MRR, nDCG, Faithfulness, Answer Relevance | F. Advanced RAG | Python |
| 69 | End-to-End RAG System | F. Advanced RAG | Python |
| 70 | Task Spec Format | G. Eval framework | Python |
| 71 | Classical Metrics | G. Eval framework | Python |
| 72 | Code Exec Metric | G. Eval framework | Python |
| 73 | Perplexity and Calibration | G. Eval framework | Python |
| 74 | Leaderboard Aggregation | G. Eval framework | Python |
| 75 | End-to-End Eval Runner | G. Eval framework | Python |
| 76 | Collective Ops From Scratch | H. Distributed train | Python |
| 77 | Data Parallel DDP From Scratch | H. Distributed train | Python |
| 78 | ZeRO Optimizer State Sharding | H. Distributed train | Python |
| 79 | Pipeline Parallel and Bubble Analysis | H. Distributed train | Python |
| 80 | Sharded Checkpoint and Atomic Resume | H. Distributed train | Python |
| 81 | End-to-End Distributed Training | H. Distributed train | Python |
| 82 | Jailbreak Taxonomy | I. Safety harness | Python |
| 83 | Prompt Injection Detector | I. Safety harness | Python |
| 84 | Refusal Evaluation | I. Safety harness | Python |
| 85 | Content Classifier Integration | I. Safety harness | Python |
| 86 | Constitutional Rules Engine | I. Safety harness | Python, YAML |
| 87 | End-to-End Safety Gate | I. Safety harness | Python |

</details>

text
โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’

The toolkit

Every lesson produces a reusable artifact. By the end you have:

text
outputs/
โ”œโ”€โ”€ prompts/      prompt templates for every AI task
โ””โ”€โ”€ skills/       SKILL.md files for AI coding agents

Plug them into Claude, Cursor, Codex, OpenClaw, Hermes, or any agent that
reads a SKILL.md / AGENTS.md directory. Real tools, not homework.

Install course skills into your agent

Two skill sets, two installers:

The learning skills (/start-learning, /learn, /course-guide,
/claude-certification, /find-your-level, /check-understanding) live under skills/
and install into any agent with one command โ€” no clone, no Python:

bash
npx skills add rohitg00/ai-engineering-from-scratch

skills writes to whichever directory your agent picks up: .claude/skills/,
.cursor/skills/, .codex/skills/, OpenClaw's skills folder, Hermes's bundle
path, or any SKILL.md-aware tool. One command, every agent.

The lesson artifacts. The repo ships 388 skills and 99 prompts under
phases//outputs/; install them via scripts/install_skills.py. Requires
cloning the repo. Supports tag filters, dry-runs, and per-agent layouts:

bash
python3 scripts/install_skills.py <target>                                 # every skill, default --layout skills (nested)
python3 scripts/install_skills.py <target> --layout skills                 # same as above, explicit
python3 scripts/install_skills.py <target> --type all                      # skills + prompts + agents
python3 scripts/install_skills.py <target> --phase 14                      # one phase only
python3 scripts/install_skills.py <target> --tag rag                       # filter by tag
python3 scripts/install_skills.py <target> --layout flat                   # flat files
python3 scripts/install_skills.py <target> --dry-run                       # preview without writing
python3 scripts/install_skills.py <target> --force                         # overwrite existing files

<target> is the skills directory for your agent (examples:
~/.claude/skills/, ~/.cursor/skills/, ~/.config/openclaw/skills/,
.skills/, or any path your agent reads).

By default the script refuses to overwrite an existing destination and exits
with code 1 after listing every colliding path. Use --dry-run to preview
collisions or --force to overwrite. Every non-dry-run run writes a
manifest.json in the target with the full inventory grouped by type and
phase. Pick the layout your agent reads:

| --layout | Path written |
|---|---|
| skills | <target>/<name>/SKILL.md (nested convention, supported by Claude / Cursor / Codex / OpenClaw / Hermes) |
| by-phase | <target>/phase-NN/<name>.md |
| flat | <target>/<name>.md |

Drop the agent workbench into your own repo

The Phase 14 capstone ships a reusable Agent Workbench pack (AGENTS.md, schemas,
init / verify / handoff scripts). Scaffold it into any repo with:

bash
python3 scripts/scaffold_workbench.py path/to/your-repo            # full pack + seeds
python3 scripts/scaffold_workbench.py path/to/your-repo --minimal  # skip docs/
python3 scripts/scaffold_workbench.py path/to/your-repo --dry-run  # preview only
python3 scripts/scaffold_workbench.py path/to/your-repo --force    # overwrite

You get the seven workbench surfaces wired up, a starter task_board.json,
and a fresh agent_state.json at schema_version: 1. From there: edit the
task, edit AGENTS.md, run scripts/init_agent.py, hand the contract to
your agent. The pack source lives at
phases/14-agent-engineering/42-agent-workbench-capstone/outputs/agent-workbench-pack/.

Browse the entire course as JSON

scripts/build_catalog.py walks every phase, every lesson, every artifact on
disk and writes catalog.json at the repo root. One file, every course truth.

bash
python3 scripts/build_catalog.py               # writes <repo>/catalog.json
python3 scripts/build_catalog.py --stdout      # to stdout, do not touch repo
python3 scripts/build_catalog.py --out path/to/file.json

The catalog is filesystem-derived, not README-derived, so counts always match
what is actually on disk. Use it for site builds, downstream tooling, or to
verify the README counts have not drifted. Schema is documented at the top of
the script.

A GitHub Action (.github/workflows/curriculum.yml) rebuilds catalog.json
on every PR and fails the build if the committed file is stale. After editing
any lesson, run python3 scripts/build_catalog.py and commit the result, or
CI will reject the PR. The same workflow runs audit_lessons.py in
warn-only mode (so existing drift does not block contributors).

Smoke-check every lesson's Python code

scripts/lesson_run.py byte-compiles every .py file under each lesson's
code/ directory. Default mode is syntax-check only โ€” no execution, no API
keys, no heavy ML deps required. Catches the regressions contributors
introduce most often (bad indentation, broken f-strings, stray edits).

bash
python3 scripts/lesson_run.py                  # syntax-check the whole curriculum
python3 scripts/lesson_run.py --phase 14       # one phase only
python3 scripts/lesson_run.py --json           # JSON report on stdout
python3 scripts/lesson_run.py --strict         # exit 1 if any lesson fails
python3 scripts/lesson_run.py --execute        # actually run, 10s timeout per lesson

--execute runs each lesson's code/main.py (or the first .py file) with a
10-second timeout. Lessons whose entry file starts with a # requires: pkg1,
pkg2 comment listing non-stdlib deps are skipped with reason needs <deps>.
The script is opt-in and not wired into CI.

Stdlib only, Python 3.10+. Set LINK_CHECK_SKIP=domain1,domain2 to override
the default skip-list (twitter.com, x.com, linkedin.com,
instagram.com, medium.com โ€” domains that aggressively block automated
HEAD/GET).

Where to start

| Background | Start at | Estimated time |
|---|---|---|
| New to programming and AI | Phase 0 โ€” Setup | ~306 hours |
| Know Python, new to ML | Phase 1 โ€” Math Foundations | ~270 hours |
| Know ML, new to deep learning | Phase 3 โ€” Deep Learning Core | ~200 hours |
| Know deep learning, want LLMs and agents | Phase 10 โ€” LLMs from Scratch | ~100 hours |
| Senior engineer, only want agent engineering | Phase 14 โ€” Agent Engineering | ~60 hours |

text
โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’

Why this matters now

<table>
<tr>
<th align="left" width="50%"><sub>FIG_003 ยท A</sub><br/><b>THE INDUSTRY SIGNAL</b></th>
<th align="left" width="50%"><sub>FIG_003 ยท B</sub><br/><b>FOUNDATIONAL PAPERS COVERED</b></th>
</tr>
<tr>
<td valign="top">

"The hottest new programming language is English."<br/>

โ€” Andrej Karpathy (tweet)

> "Software engineering is being remade in front of our eyes."<br/>

โ€” Boris Cherny, creator of Claude Code

> "Models will keep getting better. The skill that compounds is knowing what to build."<br/>

โ€” Industry consensus, 2026

</td>
<td valign="top">

- Attention Is All You Need โ€” Vaswani et al., 2017 โ†’ Phase 7
- Language Models are Few-Shot Learners (GPT-3) โ†’ Phase 10
- Denoising Diffusion Probabilistic Models โ†’ Phase 8
- InstructGPT / RLHF โ†’ Phase 10
- Direct Preference Optimization โ†’ Phase 10
- Chain-of-Thought Prompting โ†’ Phase 11
- ReAct: Reasoning + Acting in LLMs โ†’ Phase 14
- Model Context Protocol โ€” Anthropic โ†’ Phase 13

</td>
</tr>
</table>

text
โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’

Contributing

| Goal | Read |
|---|---|
| Contribute a lesson or fix | CONTRIBUTING.md |
| Fork for your team or school | FORKING.md |
| Lesson template | LESSON_TEMPLATE.md |
| Track progress | ROADMAP.md |
| Glossary | glossary/terms.md |
| Code of conduct | CODE_OF_CONDUCT.md |

Before submitting a lesson, run the invariant check:

bash
python3 scripts/audit_lessons.py           # full curriculum
python3 scripts/audit_lessons.py --phase 14  # single phase
python3 scripts/audit_lessons.py --json    # CI-friendly output

Exit code is non-zero when any rule fails. Rules (L001โ€“L010) validate directory
shape, docs/en.md presence + H1, code/ non-emptiness, quiz.json schema
(rejects the legacy q/choices/answer keys that caused issue #102), and
relative links inside lesson docs.

text
โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’

Free, MIT-licensed, 503 lessons. The curriculum is maintained on sponsorship alone. Cash only.

Reach (verified 2026-05-14): 55,593 monthly visitors ยท 90,709 page views ยท 7.5K stars ยท
Twitter/X is the #1 acquisition channel.

<br />
<br />
<a href="https://vercel.com/open-source-program">
<img alt="Vercel OSS Program" src="https://vercel.com/oss/program-badge-2026.svg" />
</a>

Current sponsors: CodeRabbit ยท iii

| Tier | $/mo | What you get |
|------|------|---|
| Backer | $25 | Name in BACKERS.md |
| Bronze | $250 | Text-only row in README sponsor block + launch-day tweet |
| Silver | $750 | Small logo in README + listed as one supported provider in API lessons |
| Gold | $2,000 | Medium logo in README + sponsor page + quarterly X / LinkedIn co-feature |
| Platinum | $5,000 | Hero logo above the fold + one dedicated integration lesson, max 1 partner |

Full rate card, hard rules, pricing anchors, and reach data: SPONSORS.md.
Sign up via GitHub Sponsors.

text
โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’โ–‘โ–‘โ–‘โ–’โ–’โ–’

Star history

<a href="https://star-history.com/#rohitg00/ai-engineering-from-scratch&Date">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=rohitg00/ai-engineering-from-scratch&type=Date&theme=dark">
<img alt="Star history" src="https://api.star-history.com/svg?repos=rohitg00/ai-engineering-from-scratch&type=Date" width="100%">
</picture>
</a>

If this manual helped you, star the repo. It keeps the project alive.

License

MIT. Use it however you want โ€” fork it, teach it, sell it, ship it. Attribution appreciated,
not required.

Maintained by Rohit Ghumare and the community.

<sub>
<a href="https://x.com/ghumare64">@ghumare64</a> &nbsp;ยท&nbsp;
<a href="https://aiengineeringfromscratch.com">aiengineeringfromscratch.com</a> &nbsp;ยท&nbsp;
<a href="https://github.com/rohitg00/ai-engineering-from-scratch/issues/new/choose">Report / Suggest</a>
</sub>