### I18n # Internationalization (i18n) The English lessons are canonical. Translated **lesson** markdown is machine-generated and **never committed to `main`** — it lives on a separate `translations` branch, and the site fetches it at runtime the same free way it fetches English (`raw.githubusercontent.com`). No Vercel functions, no KV, no per-view cost. The one exception is the per-language **README**, which is hand-authored for quality and committed to `main` (see below). This pipeline covers lessons under `phases/` only. Certification curricula remain English-only and are intentionally excluded from both machine translation and book generation. The default engine is **NLLB-200** ([No Language Left Behind, Meta AI, 2022](https://arxiv.org/abs/2207.04672)), an open translation model covering 200 languages, which runs **inside the GitHub Actions runner with no API key and no bill**. Compute is the free CI runner; there is no translation-API cost at all. Paid LLM providers (Anthropic/OpenAI) and DeepL remain available as optional higher-quality upgrades via `--provider`. ## Pieces The `languages.json` registry drives the machine-translation surfaces — lessons, the site switcher, and the book matrix. The per-language **README is separate**: its language set and translated blocks live in `scripts/readme_translations.py`, hand-authored for quality and independent of the registry. | Piece | Where | Committed to main? | |-------|-------|--------------------| | Language registry | `languages.json` | Yes (one small file) | | Lesson translation script | `scripts/translate_lessons.py` (prose-only walker) | Yes | | README generator + translations | `scripts/build_readme_i18n.py` + `scripts/readme_translations.py` (hand-authored) | Yes | | CI workflow | `.github/workflows/translate.yml` (sharded per language × phase) | Yes | | Translated lesson markdown | `translations` branch, `i18n//…/.md` | No (separate branch) | | Translated README | `i18n//README.md`, linked from the header of `README.md` | **Yes** | | Translated books | `scripts/build_book.py --lang ` reads the translated markdown and emits `aiefs-vol{n}-{slug}-.epub/.pdf` as release assets (English fallback per lesson) | No (release assets) | | Switcher options | `site/langs.js` (generated by `build.js` from the `source`/`ci:true` set) | No (gitignored, like `data.js`) | | Site switcher | `site/lesson.html` | Yes (the UI + fetch logic) | ## Add a language Add one entry to `languages.json`, the single source of truth. Fields: - `code` — the tag used in filenames, URLs, and the switcher value. - `name` — English name, used in the LLM-provider prompt. - `native` — label shown in the switcher. - `nllb` — the FLORES-200 code (e.g. `zho_Hans`, `tur_Latn`); **required** for the default NLLB provider — `translate_lessons.py` exits if a requested language has none. - `ci` — `true` opts the language into the auto push-triggered workflow's build matrix. `workflow_dispatch` can request any registered language regardless. Both matrices read from `ci: true`, so the same flag opts a language into lesson translation **and** into the site switcher — `build.js` only emits `source`/`ci:true` languages into `site/langs.js`, so the switcher never offers a language the site can't serve. The README bar is independent: it can list any language `readme_translations.py` covers. Nothing else to touch. ## How it runs 1. A push to `main` that changes `phases/**/docs/en.md`, `languages.json`, or the translation workflow itself triggers `.github/workflows/translate.yml`. Certification lesson changes do not trigger it. 2. The matrix is **one job per (language, phase)**. A full 503-lesson language run is ~27h on a CPU runner, far past the 6-hour job limit, so a per-language job always timed out before publishing. Splitting by phase keeps the largest job (phase 19, 85 lessons) near ~4.5h, well under the limit. Each job restores its language's prior output from the branch, runs `translate_lessons.py --phase `, and pushes only its own `i18n//phases//` slice back with a fetch-and-retry loop, so disjoint slices merge without collisions. 3. The site's language switcher sets `?lang=`; `fetchLesson` pulls `…/translations/i18n//…/.md` and **falls back to canonical English** if that translation isn't published yet. English (`lang=en`) is byte-identical to the pre-i18n fetch path. ## Never wastes a run Every lesson is keyed by the `sha256` of its English source in a per-(language, phase) cache (`i18n//.cache/.json`), written per lesson in the runner and **published to the `translations` branch when the job finishes**. So: - The first run translates everything once. Every completed run after it re-translates **only** lessons whose English actually changed — everything else is a cache hit and is skipped instantly. - A cancelled or interrupted phase job does not publish, so it loses that job's runner-local progress and the next run resumes that phase from the **last published** cache. Because each shard is a single phase, it comfortably finishes and publishes within the job limit. ## Per-language README The README is the landing page, so its translations are **hand-authored** (highest quality) rather than machine-generated, and they live in `main` at `i18n//README.md`. `scripts/readme_translations.py` holds the translated strings keyed by the exact English block; `scripts/build_readme_i18n.py` rebuilds each file by replacing **only** the translated line-spans in a copy of the English README. Everything else — the banner, badges, the 584-row lesson table, all code and link *text* — is preserved verbatim (asserted by a round-trip identity check on every run). The one deliberate transform is on link and image *targets*: repo-root-relative paths get a `../../` prefix so they still resolve from two directories deep (fenced code is skipped, so code that looks like a link is left alone). Any block without a translation falls back to English, so coverage can grow language by language. `python3 scripts/build_readme_i18n.py --check` runs in CI (`curriculum.yml`) and fails if any committed README is stale, and the README-counts bot regenerates them whenever it syncs the English stats block. ## What is protected during translation NLLB is a sentence translator, not an instruction-following model, so the walker never lets it see anything but prose. It processes the lesson line by line: fenced code blocks, tables, image lines, and the metadata header (`**Type:** … **Time:**`) pass through verbatim; on every other line it protects inline `` `code` ``, `$math$`, image refs, full markdown links, `**bold**` terms, and URLs behind placeholders, translates only the plain-text fragments between them, then restores. Bold spans are kept because in this course they are almost always technical terms (Skip-gram, negative sampling) that should stay English. This is verified mechanically: running the walker with an identity "translation" reproduces all **503 lessons byte-for-byte**, so a real run can only ever change prose. If a placeholder fails to round-trip, that lesson keeps its English (fail-safe). ## Cost Default provider `nllb`: **free.** NLLB-200 runs in the GitHub Actions runner — no API key, no secret, no per-token bill. The only resource is free CI compute; the model is cached between runs and only changed lessons are re-translated (sha256 cache). Vercel cost does not change. Optional upgrades (`--provider anthropic|openai|deepl`) give higher fluency at low-tens-of-dollars per full pass per language, if you later want to polish a high-traffic language. Those providers read the key from the `LLM_API_KEY` environment variable (DeepL uses the same variable for its auth key). NLLB stays the zero-cost default. ## Quality sample Below is `05 · 03 Word Embeddings` translated through Step 1, showing that prose reads naturally while code, math, `king - man + woman`, technical terms, and the metadata header survive untouched. This sample is LLM-tier fluency (what `--provider anthropic` produces); the free NLLB-200 default is solid and fully faithful on the preservation guarantee, with slightly plainer phrasing. Upgrade a high-traffic language to an LLM provider later if you want this level of polish. ### 简体中文 (zh) > # 词嵌入 —— 从零实现 Word2Vec > > > 词以其所处的语境为意。用一个浅层网络训练这个想法,几何结构便会自然浮现。 > > **Type:** Build · **Languages:** Python · **Prerequisites:** 阶段 5 · 02(BoW + TF-IDF)、阶段 3 · 03(从零实现反向传播) · **Time:** 约 75 分钟 > > **问题** —— TF-IDF 知道 `dog` 和 `puppy` 是不同的词,但不知道二者含义几乎相同。在 `dog` 上训练的分类器无法泛化到讨论 `puppy` 的评论。你可以靠列举同义词勉强弥补,但这在罕见词、领域术语,以及所有你未曾预料的语言上都会失效。 > > 你想要的是这样一种表示:`dog` 和 `puppy` 在空间中彼此靠近;`king - man + woman` 落在 `queen` 附近;在 `dog` 上训练的模型能免费地把部分信号迁移到 `puppy`。 > > **核心概念** —— **分布假说**(Firth,1957):"要了解一个词,就看它与哪些词为伴。" Word2Vec 有两种形式:**Skip-gram** 给定中心词预测周围词(窗口为 2 时 `cat -> (the, sat, on)`);**CBOW(连续词袋)** 给定周围词预测中心词。网络只有一个隐藏层且无非线性:输入是词表上的 one-hot 向量,输出是 softmax,训练后丢弃输出层,隐藏层权重即为嵌入。诀窍是用**负采样(negative sampling)**把对 10 万词的 softmax 转成二分类。 ### Türkçe (tr) > # Kelime Gömmeleri —— Word2Vec'i Sıfırdan > > > Bir kelime, birlikte bulunduğu kelimelerden ibarettir. Bu fikri sığ bir ağ ile eğitin, geometri kendiliğinden ortaya çıkar. > > **Type:** Build · **Languages:** Python · **Prerequisites:** Aşama 5 · 02 (BoW + TF-IDF), Aşama 3 · 03 (Sıfırdan Geri Yayılım) · **Time:** ~75 dakika > > **Problem** —— TF-IDF, `dog` ve `puppy`'nin farklı kelimeler olduğunu bilir ama neredeyse aynı anlama geldiklerini bilmez. `dog` üzerinde eğitilen bir sınıflandırıcı, `puppy` hakkındaki bir yoruma genelleme yapamaz. Eş anlamlıları listeleyerek geçiştirebilirsiniz, ama bu; nadir terimlerde, alana özgü jargonda ve öngöremediğiniz her dilde başarısız olur. > > `dog` ve `puppy`'nin uzayda birbirine yakın düştüğü, `king - man + woman`'ın `queen`'e yakın indiği bir temsil istiyorsunuz. Word2Vec bunu verdi: iki katmanlı bir ağ, trilyon token'lık eğitim, 2013'te yayımlandı. > > **Kavram** —— **Dağılımsal hipotez** (Firth, 1957): "Bir kelimeyi birlikte bulunduğu kelimelerden tanırsın." İki tür: **Skip-gram** merkez kelimeden çevredekileri tahmin eder (pencere 2 ile `cat -> (the, sat, on)`); **CBOW** çevredekilerden merkezi tahmin eder. Ağın doğrusal olmayan aktivasyonu olmayan tek bir gizli katmanı vardır: girdi one-hot vektör, çıktı softmax'tır; eğitimden sonra çıktı katmanı atılır, gizli katman ağırlıkları gömmelerdir. Püf nokta, 100 bin kelimelik softmax'ı **negatif örnekleme (negative sampling)** ile ikili sınıflandırmaya çevirmektir. Note how `dog`, `puppy`, `king - man + woman`, `softmax`, `one-hot`, `Skip-gram`, `CBOW`, `TF-IDF`, `Word2Vec`, and the `**Type:** / **Time:**` labels stay verbatim in both languages while the surrounding prose is fully localized. That preservation is enforced mechanically by the placeholder round-trip, not left to the model. --- ### CHANGELOG # Changelog What's new in the curriculum. Most recent first. Format loosely follows [Keep a Changelog](https://keepachangelog.com/). Each entry names the phase, lesson, and what changed, so learners can jump straight to the delta. ## [Unreleased] ### Added - `scripts/scaffold-lesson.sh` — scaffolder that creates `phases/NN-phase/NN-lesson/` with the full folder structure and a `docs/en.md` skeleton prefilled from `LESSON_TEMPLATE.md`. - `.github/PULL_REQUEST_TEMPLATE.md` — contributor checklist (code runs, no code comments, built-from-scratch-first, atomic per-lesson commit, markdown-link ROADMAP row). - `.github/ISSUE_TEMPLATE/bug_report.md` and `new_lesson_proposal.md` — structured intake for bug reports and lesson pitches. - This `CHANGELOG.md`. ## 2026-04 — Phase 4: Computer Vision complete ### Added - All 28 Phase 4 lessons, covering image fundamentals through multi-modal vision (VLMs, 3D, video, self-supervised). - Phase 4 rows in `ROADMAP.md` linked as markdown to the lesson folders, so the website surfaces them. ### Fixed - Phase 4 precision pass across 15+ lessons: - `phase-4/02`: shape calculator specifies RF/stride handling for adaptive pool, flatten, and linear. - `phase-4/03`: backbone selector description lists all covered families; head guidance added for OCR, medical, industrial. - `phase-4/04`: classification diagnostics use quantitative thresholds per failure mode; `n/a` declared for undefined metrics; guard for fewer than 3 classes. - `phase-4/06`: detection metric reader uses `AP@0.5` (not `mAP@0.5`); per-class recall declared optional; anchor designer clarifies stride truncation and single-anchor-per-level path. - `phase-4/10`: sampler picker declares `unet_forward_ms` as an input; ControlNet guard promoted to rule 0. - `phase-4/14`: ViT inspector aligned with refusal rule — port attempts are audited, not endorsed. - `phase-4/24`: open-vocab stack picker has explicit rule precedence and license-filter semantics; concept designer resolves step-5/rule-80 conflict. - `phase-4/25`: VLM docs `_merge` raises descriptive `ValueError` on placeholder mismatch; CMER normalises internally. - `phase-4/27`: `synthetic_frames` clips GT boxes to frame H/W. - `phase-4/28`: `rope_3d` validates dim split; dropped unused `F` import from DiT block example. ## 2026-Q1 and earlier ### Added - Phase 0 (Setup & Tooling): all 12 lessons. - Phase 1 (Math Foundations): all 22 lessons. - Phase 2 (ML Fundamentals): all 18 lessons. - Phase 3 (Deep Learning Core): core lessons through perceptron, backprop, optimizers. - Built-in Claude Code skills: `find-your-level` (placement quiz) and `check-understanding` (per-phase quiz). - Website at `aiengineeringfromscratch.com`: catalog, per-lesson pages, roadmap, 277-term glossary. - Initial scaffolding for all 20 phases (`phases/00-*` through `phases/19-*`). - `LESSON_TEMPLATE.md`, `CONTRIBUTING.md`, `ROADMAP.md`, `README.md`. [Unreleased]: https://github.com/rohitg00/ai-engineering-from-scratch/compare/HEAD...HEAD --- ### CONTRIBUTING # Contributing Lessons, translations, fixes, outputs — all welcome. One contribution per pull request keeps reviews fast and lets contributor counts and credit work correctly. ## Important: the README and ROADMAP feed the website `site/build.js` parses `README.md`, `ROADMAP.md`, and `glossary/terms.md` to generate `site/data.js`. Two patterns must stay intact in any pull request that touches those files: - Phase headers in either `### Phase N: Name \`X lessons\`` form or `
Phase N — Name ... X lessons ... Description` form. - Lesson tables with the column shape `| # | Lesson | Type | Lang |` (or `| # | Project | Combines | Lang |` for capstone tables). The `Lang` column accepts plain text (`Python, TypeScript`) or the legacy emoji flags (`🐍 🟦 🦀 🟣 ⚛️`); both are parser-equivalent. - ROADMAP status glyphs (`✅`, `🚧`, `⬚`) on phase headers and lesson rows. Do not replace them with text — the parser keys off the exact characters. Run `node site/build.js` after editing those files; `git diff site/data.js` should show only the timestamp change if your edit was structural-safe. ## Ways to Contribute ### 1. Add a New Lesson Each lesson lives in `phases/XX-phase-name/NN-lesson-name/` with this structure: ``` NN-lesson-name/ ├── code/ At least one runnable implementation ├── notebook/ Jupyter notebook for experimentation (optional) ├── docs/ │ └── en.md Lesson documentation (required) └── outputs/ Prompts, skills, or agents this lesson produces (if applicable) ``` **Lesson doc format** (`en.md`): ```markdown # Lesson Title > One-line motto — the core idea in one sentence. ## The Problem Why does this matter? What can't you do without this? ## The Concept Explain with diagrams, visuals, and intuition. Code comes later. ## Build It Step-by-step implementation from scratch. ## Use It Now use a real framework or library to do the same thing. ## Ship It The prompt, skill, agent, or tool this lesson produces. ## Exercises 1. Exercise one 2. Exercise two 3. Challenge exercise ``` ### 2. Add a Translation Create a new file in any lesson's `docs/` folder: ``` docs/ ├── en.md (English — always required) ├── zh.md (Chinese) ├── ja.md (Japanese) ├── es.md (Spanish) ├── hi.md (Hindi) └── ... ``` Keep the same structure as the English version. Translate content, not code. ### 3. Add an Output If a lesson should produce a reusable prompt, skill, agent, or MCP server: 1. Create it in the lesson's `outputs/` folder 2. Add a reference in the top-level `outputs/` index **Prompt format:** ```markdown --- name: prompt-name description: What this prompt does phase: 14 lesson: 01 --- [System prompt or template here] ``` **Skill format:** ```markdown --- name: skill-name description: What this skill teaches version: 1.0.0 phase: 14 lesson: 01 tags: [agents, loops] --- [Skill content here] ``` ### 4. Fix Bugs or Improve Existing Lessons - Fix code that doesn't run - Improve explanations - Add better diagrams - Update outdated information ### 5. Add Exercises or Projects More exercises and projects are always welcome, especially ones that connect multiple phases. ## Guidelines - **Code must run.** Every code file should execute without errors with the listed dependencies. - **No comments in code.** Code should be self-explanatory. Use the docs for explanation. - **Best language for the job.** Don't force Python where TypeScript or Rust is the better choice. - **Build from scratch first.** Always implement the concept from first principles before showing the framework version. - **Keep it practical.** Theory serves practice, not the other way around. - **No AI slop.** Write like a human. Be direct. Cut filler. ## Pull Request Process 1. Fork the repository 2. Create a feature branch (`git checkout -b add-lesson-phase3-gradient-descent`) 3. Make your changes 4. Ensure all code runs 5. Submit a pull request with a clear description ## Code of Conduct See [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md). Be kind, be helpful, be constructive. ## Style - Direct prose. Cut filler. Match the manual's tone, not marketing copy. - No decorative emojis in headings. Lang column emoji flags are the one exception and only because the parser maps them. - Code runs as-is with the dependencies listed in the lesson. - Build from scratch first, framework second. --- ### README

AI Engineering from Scratch — reference manual banner

Read in your language: Español · Français · Português · Deutsch · Italiano · 简体中文 · 日本語 · 한국어 · हिन्दी · العربية · Русский · Türkçe
Translated landing pages, committed to the repo. English is canonical; lesson pages are machine-translated on the translations branch. See docs/i18n.md.

MIT License 503 lessons 20 phases GitHub stars Website

## From the creator of [Agent Memory - #1 Persistent memory ⭐](https://github.com/rohitg00/agentmemory) GitHub stars which naturally works with any agents or chat assistants. ```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` > **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.

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## 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 — concept, math, code, quiz — streaming lessons straight from this repo, and `/course-guide ` 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 — the installer asks which agents to set up. No agent? Read the same lessons at [aiengineeringfromscratch.com](https://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 ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## 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 — Setup & Tooling"] --> P1["Phase 1 — Math Foundations"] P1 --> P2["Phase 2 — ML Fundamentals"] P2 --> P3["Phase 3 — Deep Learning Core"] P3 --> P4["Phase 4 — Vision"] P3 --> P5["Phase 5 — NLP"] P3 --> P6["Phase 6 — Speech & Audio"] P3 --> P9["Phase 9 — RL"] P5 --> P7["Phase 7 — Transformers"] P7 --> P8["Phase 8 — GenAI"] P7 --> P10["Phase 10 — LLMs from Scratch"] P10 --> P11["Phase 11 — LLM Engineering"] P10 --> P12["Phase 12 — Multimodal"] P11 --> P13["Phase 13 — Tools & Protocols"] P13 --> P14["Phase 14 — Agent Engineering"] P14 --> P15["Phase 15 — Autonomous Systems"] P15 --> P16["Phase 16 — Multi-Agent & Swarms"] P14 --> P17["Phase 17 — Infrastructure & Production"] P15 --> P18["Phase 18 — Ethics & Alignment"] P16 --> P19["Phase 19 — Capstone Projects"] P17 --> P19 P18 --> P19 ``` ```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## The shape of a lesson Each lesson lives in its own folder, with the same structure across the entire curriculum: ```text phases/-/-/ ├── code/ runnable implementations (Python, TypeScript, Rust, Julia) ├── docs/ │ └── en.md lesson narrative └── outputs/ prompts, skills, agents, or MCP servers this lesson produces ``` Every lesson follows six beats. The *Build It / Use It* split is the spine — 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
one-line core idea"] --> Pr["PROBLEM
concrete pain"] Pr --> C["CONCEPT
diagrams & intuition"] C --> B["BUILD IT
raw math, no frameworks"] B --> U["USE IT
same thing in PyTorch / sklearn"] U --> S["SHIP IT
prompt · skill · agent · MCP"] ``` ## Getting started Three ways in. Pick one. **Option A — 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 — no clone needed. Progress lives in `LEARNING.md` in your project, so every session resumes where you left off. **Option B — read.** Open any completed lesson on [aiengineeringfromscratch.com](https://aiengineeringfromscratch.com) or expand a phase under [Contents](#contents). No setup, no cloning. **Option C — 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](certifications/claude/README.md) 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](certifications/claude/GETTING_STARTED.md) 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](https://aiengineeringfromscratch.com/certifications.html). 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, …) | Skill | What it does | |---|---| | [`/start-learning`](skills/start-learning/SKILL.md) | One-time onboarding: why you're learning, placement quiz, personalized plan saved to `LEARNING.md`. | | [`/learn`](skills/learn/SKILL.md) | The tutor loop. Warm-up recall, then the next lesson taught interactively, then its quiz; records progress and a review queue. | | [`/course-guide`](skills/course-guide/SKILL.md) | Topic router. "Where do I learn attention?" or "my loss is NaN" → the exact lessons, with links. | | [`/claude-certification`](skills/claude-certification/SKILL.md) | 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`](skills/find-your-level/SKILL.md) | Ten-question placement quiz. Maps your knowledge to a starting phase and produces a personalized path with hour estimates. | | [`/check-understanding `](skills/check-understanding/SKILL.md) | Per-phase quiz, eight questions, with feedback and specific lessons to review. | ```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## 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](https://github.com/rohitg00/ai-engineering-from-scratch/releases); 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 · Math, Tooling, and Classical Machine Learning | 00-02 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol1-foundations.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol1-foundations.pdf) | | 2 | Deep Learning · Networks, Vision, and Speech | 03, 04, 06 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol2-deep-learning.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol2-deep-learning.pdf) | | 3 | Language · NLP Foundations and the Transformer | 05, 07 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol3-language.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol3-language.pdf) | | 4 | Large Language Models · Generation, Reinforcement, Pretraining, and Engineering | 08-11 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol4-llms.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol4-llms.pdf) | | 5 | Agents · Multimodality, Protocols, Autonomy, and Swarms | 12-16 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol5-agents.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol5-agents.pdf) | | 6 | Production · Infrastructure, Safety, and Capstones | 17-19 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol6-production.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol6-production.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](book/README.md). ```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## 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.
FIG_001.A prompts
FIG_001 · A
PROMPTS		 FIG_001.B skills
FIG_001 · B
SKILLS		 FIG_001.C agents
FIG_001 · C
AGENTS		 FIG_001.D MCP servers
FIG_001 · D
MCP SERVERS
Paste into any AI assistant for expert-level help on a narrow task. Drop into Claude, Cursor, Codex, OpenClaw, Hermes, or any agent that reads SKILL.md. Deploy as autonomous workers — you wrote the loop yourself in Phase 14. Plug into any MCP-compatible client. Built end-to-end in Phase 13.
> Install the lot with `python3 scripts/install_skills.py `. 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 · A worked sample Phase 14, lesson 1: the agent loop. ~120 lines of pure Python, no dependencies.
**`code/agent_loop.py`**   build it ```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 = tools[call.name](**call.args) history.append(tool_result(call.id, result)) continue return msg.content raise StepLimitExceeded ``` **`outputs/skill-agent-loop.md`**   ship it ```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... ```
```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## Contents Twenty phases. Click any phase to expand its lesson list. ### Phase 0: Setup & Tooling `12 lessons` > Get your environment ready for everything that follows. | # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Dev Environment](phases/00-setup-and-tooling/01-dev-environment/) | Build | Python | | 02 | [Git & Collaboration](phases/00-setup-and-tooling/02-git-and-collaboration/) | Learn | — | | 03 | [GPU Setup & Cloud](phases/00-setup-and-tooling/03-gpu-setup-and-cloud/) | Build | Python | | 04 | [APIs & Keys](phases/00-setup-and-tooling/04-apis-and-keys/) | Build | Python | | 05 | [Jupyter Notebooks](phases/00-setup-and-tooling/05-jupyter-notebooks/) | Build | Python | | 06 | [Python Environments](phases/00-setup-and-tooling/06-python-environments/) | Build | Shell | | 07 | [Docker for AI](phases/00-setup-and-tooling/07-docker-for-ai/) | Build | Docker | | 08 | [Editor Setup](phases/00-setup-and-tooling/08-editor-setup/) | Build | — | | 09 | [Data Management](phases/00-setup-and-tooling/09-data-management/) | Build | Python | | 10 | [Terminal & Shell](phases/00-setup-and-tooling/10-terminal-and-shell/) | Learn | — | | 11 | [Linux for AI](phases/00-setup-and-tooling/11-linux-for-ai/) | Learn | — | | 12 | [Debugging & Profiling](phases/00-setup-and-tooling/12-debugging-and-profiling/) | Build | Python |
Phase 1 — Math Foundations  22 lessons  The intuition behind every AI algorithm, through code.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Linear Algebra Intuition](phases/01-math-foundations/01-linear-algebra-intuition/) | Learn | Python, Julia | | 02 | [Vectors, Matrices & Operations](phases/01-math-foundations/02-vectors-matrices-operations/) | Build | Python, Julia | | 03 | [Matrix Transformations & Eigenvalues](phases/01-math-foundations/03-matrix-transformations/) | Build | Python, Julia | | 04 | [Calculus for ML: Derivatives & Gradients](phases/01-math-foundations/04-calculus-for-ml/) | Learn | Python | | 05 | [Chain Rule & Automatic Differentiation](phases/01-math-foundations/05-chain-rule-and-autodiff/) | Build | Python | | 06 | [Probability & Distributions](phases/01-math-foundations/06-probability-and-distributions/) | Learn | Python | | 07 | [Bayes' Theorem & Statistical Thinking](phases/01-math-foundations/07-bayes-theorem/) | Build | Python | | 08 | [Optimization: Gradient Descent Family](phases/01-math-foundations/08-optimization/) | Build | Python | | 09 | [Information Theory: Entropy, KL Divergence](phases/01-math-foundations/09-information-theory/) | Learn | Python | | 10 | [Dimensionality Reduction: PCA, t-SNE, UMAP](phases/01-math-foundations/10-dimensionality-reduction/) | Build | Python | | 11 | [Singular Value Decomposition](phases/01-math-foundations/11-singular-value-decomposition/) | Build | Python, Julia | | 12 | [Tensor Operations](phases/01-math-foundations/12-tensor-operations/) | Build | Python | | 13 | [Numerical Stability](phases/01-math-foundations/13-numerical-stability/) | Build | Python | | 14 | [Norms & Distances](phases/01-math-foundations/14-norms-and-distances/) | Build | Python | | 15 | [Statistics for ML](phases/01-math-foundations/15-statistics-for-ml/) | Build | Python | | 16 | [Sampling Methods](phases/01-math-foundations/16-sampling-methods/) | Build | Python | | 17 | [Linear Systems](phases/01-math-foundations/17-linear-systems/) | Build | Python | | 18 | [Convex Optimization](phases/01-math-foundations/18-convex-optimization/) | Build | Python | | 19 | [Complex Numbers for AI](phases/01-math-foundations/19-complex-numbers/) | Learn | Python | | 20 | [The Fourier Transform](phases/01-math-foundations/20-fourier-transform/) | Build | Python | | 21 | [Graph Theory for ML](phases/01-math-foundations/21-graph-theory/) | Build | Python | | 22 | [Stochastic Processes](phases/01-math-foundations/22-stochastic-processes/) | Learn | Python |
Phase 2 — ML Fundamentals  18 lessons  Classical ML — still the backbone of most production AI.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [What Is Machine Learning](phases/02-ml-fundamentals/01-what-is-machine-learning/) | Learn | Python | | 02 | [Linear Regression from Scratch](phases/02-ml-fundamentals/02-linear-regression/) | Build | Python | | 03 | [Logistic Regression & Classification](phases/02-ml-fundamentals/03-logistic-regression/) | Build | Python | | 04 | [Decision Trees & Random Forests](phases/02-ml-fundamentals/04-decision-trees/) | Build | Python | | 05 | [Support Vector Machines](phases/02-ml-fundamentals/05-support-vector-machines/) | Build | Python | | 06 | [KNN & Distance Metrics](phases/02-ml-fundamentals/06-knn-and-distances/) | Build | Python | | 07 | [Unsupervised Learning: K-Means, DBSCAN](phases/02-ml-fundamentals/07-unsupervised-learning/) | Build | Python | | 08 | [Feature Engineering & Selection](phases/02-ml-fundamentals/08-feature-engineering/) | Build | Python | | 09 | [Model Evaluation: Metrics, Cross-Validation](phases/02-ml-fundamentals/09-model-evaluation/) | Build | Python | | 10 | [Bias, Variance & the Learning Curve](phases/02-ml-fundamentals/10-bias-variance/) | Learn | Python | | 11 | [Ensemble Methods: Boosting, Bagging, Stacking](phases/02-ml-fundamentals/11-ensemble-methods/) | Build | Python | | 12 | [Hyperparameter Tuning](phases/02-ml-fundamentals/12-hyperparameter-tuning/) | Build | Python | | 13 | [ML Pipelines & Experiment Tracking](phases/02-ml-fundamentals/13-ml-pipelines/) | Build | Python | | 14 | [Naive Bayes](phases/02-ml-fundamentals/14-naive-bayes/) | Build | Python | | 15 | [Time Series Fundamentals](phases/02-ml-fundamentals/15-time-series/) | Build | Python | | 16 | [Anomaly Detection](phases/02-ml-fundamentals/16-anomaly-detection/) | Build | Python | | 17 | [Handling Imbalanced Data](phases/02-ml-fundamentals/17-imbalanced-data/) | Build | Python | | 18 | [Feature Selection](phases/02-ml-fundamentals/18-feature-selection/) | Build | Python |
Phase 3 — Deep Learning Core  13 lessons  Neural networks from first principles. No frameworks until you build one.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [The Perceptron: Where It All Started](phases/03-deep-learning-core/01-the-perceptron/) | Build | Python | | 02 | [Multi-Layer Networks & Forward Pass](phases/03-deep-learning-core/02-multi-layer-networks/) | Build | Python | | 03 | [Backpropagation from Scratch](phases/03-deep-learning-core/03-backpropagation/) | Build | Python | | 04 | [Activation Functions: ReLU, Sigmoid, GELU & Why](phases/03-deep-learning-core/04-activation-functions/) | Build | Python | | 05 | [Loss Functions: MSE, Cross-Entropy, Contrastive](phases/03-deep-learning-core/05-loss-functions/) | Build | Python | | 06 | [Optimizers: SGD, Momentum, Adam, AdamW](phases/03-deep-learning-core/06-optimizers/) | Build | Python | | 07 | [Regularization: Dropout, Weight Decay, BatchNorm](phases/03-deep-learning-core/07-regularization/) | Build | Python | | 08 | [Weight Initialization & Training Stability](phases/03-deep-learning-core/08-weight-initialization/) | Build | Python | | 09 | [Learning Rate Schedules & Warmup](phases/03-deep-learning-core/09-learning-rate-schedules/) | Build | Python | | 10 | [Build Your Own Mini Framework](phases/03-deep-learning-core/10-mini-framework/) | Build | Python | | 11 | [Introduction to PyTorch](phases/03-deep-learning-core/11-intro-to-pytorch/) | Build | Python | | 12 | [Introduction to JAX](phases/03-deep-learning-core/12-intro-to-jax/) | Build | Python | | 13 | [Debugging Neural Networks](phases/03-deep-learning-core/13-debugging-neural-networks/) | Build | Python |
Phase 4 — Computer Vision  28 lessons  From pixels to understanding — image, video, 3D, VLMs, and world models.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Image Fundamentals: Pixels, Channels, Color Spaces](phases/04-computer-vision/01-image-fundamentals/) | Learn | Python | | 02 | [Convolutions from Scratch](phases/04-computer-vision/02-convolutions-from-scratch/) | Build | Python | | 03 | [CNNs: LeNet to ResNet](phases/04-computer-vision/03-cnns-lenet-to-resnet/) | Build | Python | | 04 | [Image Classification](phases/04-computer-vision/04-image-classification/) | Build | Python | | 05 | [Transfer Learning & Fine-Tuning](phases/04-computer-vision/05-transfer-learning/) | Build | Python | | 06 | [Object Detection — YOLO from Scratch](phases/04-computer-vision/06-object-detection-yolo/) | Build | Python | | 07 | [Semantic Segmentation — U-Net](phases/04-computer-vision/07-semantic-segmentation-unet/) | Build | Python | | 08 | [Instance Segmentation — Mask R-CNN](phases/04-computer-vision/08-instance-segmentation-mask-rcnn/) | Build | Python | | 09 | [Image Generation — GANs](phases/04-computer-vision/09-image-generation-gans/) | Build | Python | | 10 | [Image Generation — Diffusion Models](phases/04-computer-vision/10-image-generation-diffusion/) | Build | Python | | 11 | [Stable Diffusion — Architecture & Fine-Tuning](phases/04-computer-vision/11-stable-diffusion/) | Build | Python | | 12 | [Video Understanding — Temporal Modeling](phases/04-computer-vision/12-video-understanding/) | Build | Python | | 13 | [3D Vision: Point Clouds, NeRFs](phases/04-computer-vision/13-3d-vision-nerf/) | Build | Python | | 14 | [Vision Transformers (ViT)](phases/04-computer-vision/14-vision-transformers/) | Build | Python | | 15 | [Real-Time Vision: Edge Deployment](phases/04-computer-vision/15-real-time-edge/) | Build | Python | | 16 | [Build a Complete Vision Pipeline](phases/04-computer-vision/16-vision-pipeline-capstone/) | Build | Python | | 17 | [Self-Supervised Vision — SimCLR, DINO, MAE](phases/04-computer-vision/17-self-supervised-vision/) | Build | Python | | 18 | [Open-Vocabulary Vision — CLIP](phases/04-computer-vision/18-open-vocab-clip/) | Build | Python | | 19 | [OCR & Document Understanding](phases/04-computer-vision/19-ocr-document-understanding/) | Build | Python | | 20 | [Image Retrieval & Metric Learning](phases/04-computer-vision/20-image-retrieval-metric/) | Build | Python | | 21 | [Keypoint Detection & Pose Estimation](phases/04-computer-vision/21-keypoint-pose/) | Build | Python | | 22 | [3D Gaussian Splatting from Scratch](phases/04-computer-vision/22-3d-gaussian-splatting/) | Build | Python | | 23 | [Diffusion Transformers & Rectified Flow](phases/04-computer-vision/23-diffusion-transformers-rectified-flow/) | Build | Python | | 24 | [SAM 3 & Open-Vocabulary Segmentation](phases/04-computer-vision/24-sam3-open-vocab-segmentation/) | Build | Python | | 25 | [Vision-Language Models (ViT-MLP-LLM)](phases/04-computer-vision/25-vision-language-models/) | Build | Python | | 26 | [Monocular Depth & Geometry Estimation](phases/04-computer-vision/26-monocular-depth/) | Build | Python | | 27 | [Multi-Object Tracking & Video Memory](phases/04-computer-vision/27-multi-object-tracking/) | Build | Python | | 28 | [World Models & Video Diffusion](phases/04-computer-vision/28-world-models-video-diffusion/) | Build | Python |
Phase 5 — NLP: Foundations to Advanced  29 lessons  Language is the interface to intelligence.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Text Processing: Tokenization, Stemming, Lemmatization](phases/05-nlp-foundations-to-advanced/01-text-processing/) | Build | Python | | 02 | [Bag of Words, TF-IDF & Text Representation](phases/05-nlp-foundations-to-advanced/02-bag-of-words-tfidf/) | Build | Python | | 03 | [Word Embeddings: Word2Vec from Scratch](phases/05-nlp-foundations-to-advanced/03-word-embeddings-word2vec/) | Build | Python | | 04 | [GloVe, FastText & Subword Embeddings](phases/05-nlp-foundations-to-advanced/04-glove-fasttext-subword/) | Build | Python | | 05 | [Sentiment Analysis](phases/05-nlp-foundations-to-advanced/05-sentiment-analysis/) | Build | Python | | 06 | [Named Entity Recognition (NER)](phases/05-nlp-foundations-to-advanced/06-named-entity-recognition/) | Build | Python | | 07 | [POS Tagging & Syntactic Parsing](phases/05-nlp-foundations-to-advanced/07-pos-tagging-parsing/) | Build | Python | | 08 | [Text Classification — CNNs & RNNs for Text](phases/05-nlp-foundations-to-advanced/08-cnns-rnns-for-text/) | Build | Python | | 09 | [Sequence-to-Sequence Models](phases/05-nlp-foundations-to-advanced/09-sequence-to-sequence/) | Build | Python | | 10 | [Attention Mechanism — The Breakthrough](phases/05-nlp-foundations-to-advanced/10-attention-mechanism/) | Build | Python | | 11 | [Machine Translation](phases/05-nlp-foundations-to-advanced/11-machine-translation/) | Build | Python | | 12 | [Text Summarization](phases/05-nlp-foundations-to-advanced/12-text-summarization/) | Build | Python | | 13 | [Question Answering Systems](phases/05-nlp-foundations-to-advanced/13-question-answering/) | Build | Python | | 14 | [Information Retrieval & Search](phases/05-nlp-foundations-to-advanced/14-information-retrieval-search/) | Build | Python | | 15 | [Topic Modeling: LDA, BERTopic](phases/05-nlp-foundations-to-advanced/15-topic-modeling/) | Build | Python | | 16 | [Text Generation](phases/05-nlp-foundations-to-advanced/16-text-generation-pre-transformer/) | Build | Python | | 17 | [Chatbots: Rule-Based to Neural](phases/05-nlp-foundations-to-advanced/17-chatbots-rule-to-neural/) | Build | Python | | 18 | [Multilingual NLP](phases/05-nlp-foundations-to-advanced/18-multilingual-nlp/) | Build | Python | | 19 | [Subword Tokenization: BPE, WordPiece, Unigram, SentencePiece](phases/05-nlp-foundations-to-advanced/19-subword-tokenization/) | Learn | Python | | 20 | [Structured Outputs & Constrained Decoding](phases/05-nlp-foundations-to-advanced/20-structured-outputs-constrained-decoding/) | Build | Python | | 21 | [NLI & Textual Entailment](phases/05-nlp-foundations-to-advanced/21-nli-textual-entailment/) | Learn | Python | | 22 | [Embedding Models Deep Dive](phases/05-nlp-foundations-to-advanced/22-embedding-models-deep-dive/) | Learn | Python | | 23 | [Chunking Strategies for RAG](phases/05-nlp-foundations-to-advanced/23-chunking-strategies-rag/) | Build | Python | | 24 | [Coreference Resolution](phases/05-nlp-foundations-to-advanced/24-coreference-resolution/) | Learn | Python | | 25 | [Entity Linking & Disambiguation](phases/05-nlp-foundations-to-advanced/25-entity-linking/) | Build | Python | | 26 | [Relation Extraction & Knowledge Graph Construction](phases/05-nlp-foundations-to-advanced/26-relation-extraction-kg/) | Build | Python | | 27 | [LLM Evaluation: RAGAS, DeepEval, G-Eval](phases/05-nlp-foundations-to-advanced/27-llm-evaluation-frameworks/) | Build | Python | | 28 | [Long-Context Evaluation: NIAH, RULER, LongBench, MRCR](phases/05-nlp-foundations-to-advanced/28-long-context-evaluation/) | Learn | Python | | 29 | [Dialogue State Tracking](phases/05-nlp-foundations-to-advanced/29-dialogue-state-tracking/) | Build | Python |
Phase 6 — Speech & Audio  17 lessons  Hear, understand, speak.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Audio Fundamentals: Waveforms, Sampling, FFT](phases/06-speech-and-audio/01-audio-fundamentals) | Learn | Python | | 02 | [Spectrograms, Mel Scale & Audio Features](phases/06-speech-and-audio/02-spectrograms-mel-features) | Build | Python | | 03 | [Audio Classification](phases/06-speech-and-audio/03-audio-classification) | Build | Python | | 04 | [Speech Recognition (ASR)](phases/06-speech-and-audio/04-speech-recognition-asr) | Build | Python | | 05 | [Whisper: Architecture & Fine-Tuning](phases/06-speech-and-audio/05-whisper-architecture-finetuning) | Build | Python | | 06 | [Speaker Recognition & Verification](phases/06-speech-and-audio/06-speaker-recognition-verification) | Build | Python | | 07 | [Text-to-Speech (TTS)](phases/06-speech-and-audio/07-text-to-speech) | Build | Python | | 08 | [Voice Cloning & Voice Conversion](phases/06-speech-and-audio/08-voice-cloning-conversion) | Build | Python | | 09 | [Music Generation](phases/06-speech-and-audio/09-music-generation) | Build | Python | | 10 | [Audio-Language Models](phases/06-speech-and-audio/10-audio-language-models) | Build | Python | | 11 | [Real-Time Audio Processing](phases/06-speech-and-audio/11-real-time-audio-processing) | Build | Python | | 12 | [Build a Voice Assistant Pipeline](phases/06-speech-and-audio/12-voice-assistant-pipeline) | Build | Python | | 13 | [Neural Audio Codecs — EnCodec, SNAC, Mimi, DAC](phases/06-speech-and-audio/13-neural-audio-codecs) | Learn | Python | | 14 | [Voice Activity Detection & Turn-Taking](phases/06-speech-and-audio/14-voice-activity-detection-turn-taking) | Build | Python | | 15 | [Streaming Speech-to-Speech — Moshi, Hibiki](phases/06-speech-and-audio/15-streaming-speech-to-speech-moshi-hibiki) | Learn | Python | | 16 | [Voice Anti-Spoofing & Audio Watermarking](phases/06-speech-and-audio/16-anti-spoofing-audio-watermarking) | Build | Python | | 17 | [Audio Evaluation — WER, MOS, MMAU, Leaderboards](phases/06-speech-and-audio/17-audio-evaluation-metrics) | Learn | Python |
Phase 7 — Transformers Deep Dive  16 lessons  The architecture that changed everything.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Why Transformers: The Problems with RNNs](phases/07-transformers-deep-dive/01-why-transformers/) | Learn | Python | | 02 | [Self-Attention from Scratch](phases/07-transformers-deep-dive/02-self-attention-from-scratch/) | Build | Python | | 03 | [Multi-Head Attention](phases/07-transformers-deep-dive/03-multi-head-attention/) | Build | Python | | 04 | [Positional Encoding: Sinusoidal, RoPE, ALiBi](phases/07-transformers-deep-dive/04-positional-encoding/) | Build | Python | | 05 | [The Full Transformer: Encoder + Decoder](phases/07-transformers-deep-dive/05-full-transformer/) | Build | Python | | 06 | [BERT — Masked Language Modeling](phases/07-transformers-deep-dive/06-bert-masked-language-modeling/) | Build | Python | | 07 | [GPT — Causal Language Modeling](phases/07-transformers-deep-dive/07-gpt-causal-language-modeling/) | Build | Python | | 08 | [T5, BART — Encoder-Decoder Models](phases/07-transformers-deep-dive/08-t5-bart-encoder-decoder/) | Learn | Python | | 09 | [Vision Transformers (ViT)](phases/07-transformers-deep-dive/09-vision-transformers/) | Build | Python | | 10 | [Audio Transformers — Whisper Architecture](phases/07-transformers-deep-dive/10-audio-transformers-whisper/) | Learn | Python | | 11 | [Mixture of Experts (MoE)](phases/07-transformers-deep-dive/11-mixture-of-experts/) | Build | Python | | 12 | [KV Cache, Flash Attention & Inference Optimization](phases/07-transformers-deep-dive/12-kv-cache-flash-attention/) | Build | Python | | 13 | [Scaling Laws](phases/07-transformers-deep-dive/13-scaling-laws/) | Learn | Python | | 14 | [Build a Transformer from Scratch](phases/07-transformers-deep-dive/14-build-a-transformer-capstone/) | Build | Python | | 15 | [Attention Variants — Sliding Window, Sparse, Differential](phases/07-transformers-deep-dive/15-attention-variants/) | Build | Python | | 16 | [Speculative Decoding — Draft, Verify, Repeat](phases/07-transformers-deep-dive/16-speculative-decoding/) | Build | Python |
Phase 8 — Generative AI  15 lessons  Create images, video, audio, 3D, and more.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Generative Models: Taxonomy & History](phases/08-generative-ai/01-generative-models-taxonomy-history/) | Learn | Python | | 02 | [Autoencoders & VAE](phases/08-generative-ai/02-autoencoders-vae/) | Build | Python | | 03 | [GANs: Generator vs Discriminator](phases/08-generative-ai/03-gans-generator-discriminator/) | Build | Python | | 04 | [Conditional GANs & Pix2Pix](phases/08-generative-ai/04-conditional-gans-pix2pix/) | Build | Python | | 05 | [StyleGAN](phases/08-generative-ai/05-stylegan/) | Build | Python | | 06 | [Diffusion Models — DDPM from Scratch](phases/08-generative-ai/06-diffusion-ddpm-from-scratch/) | Build | Python | | 07 | [Latent Diffusion & Stable Diffusion](phases/08-generative-ai/07-latent-diffusion-stable-diffusion/) | Build | Python | | 08 | [ControlNet, LoRA & Conditioning](phases/08-generative-ai/08-controlnet-lora-conditioning/) | Build | Python | | 09 | [Inpainting, Outpainting & Editing](phases/08-generative-ai/09-inpainting-outpainting-editing/) | Build | Python | | 10 | [Video Generation](phases/08-generative-ai/10-video-generation/) | Build | Python | | 11 | [Audio Generation](phases/08-generative-ai/11-audio-generation/) | Build | Python | | 12 | [3D Generation](phases/08-generative-ai/12-3d-generation/) | Build | Python | | 13 | [Flow Matching & Rectified Flows](phases/08-generative-ai/13-flow-matching-rectified-flows/) | Build | Python | | 14 | [Evaluation: FID, CLIP Score](phases/08-generative-ai/14-evaluation-fid-clip-score/) | Build | Python | | 19 | [Visual Autoregressive Modeling (VAR): Next-Scale Prediction](phases/08-generative-ai/19-visual-autoregressive-var/) | Build | Python |
Phase 9 — Reinforcement Learning  12 lessons  The foundation of RLHF and game-playing AI.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [MDPs, States, Actions & Rewards](phases/09-reinforcement-learning/01-mdps-states-actions-rewards/) | Learn | Python | | 02 | [Dynamic Programming](phases/09-reinforcement-learning/02-dynamic-programming/) | Build | Python | | 03 | [Monte Carlo Methods](phases/09-reinforcement-learning/03-monte-carlo-methods/) | Build | Python | | 04 | [Q-Learning, SARSA](phases/09-reinforcement-learning/04-q-learning-sarsa/) | Build | Python | | 05 | [Deep Q-Networks (DQN)](phases/09-reinforcement-learning/05-dqn/) | Build | Python | | 06 | [Policy Gradients — REINFORCE](phases/09-reinforcement-learning/06-policy-gradients-reinforce/) | Build | Python | | 07 | [Actor-Critic — A2C, A3C](phases/09-reinforcement-learning/07-actor-critic-a2c-a3c/) | Build | Python | | 08 | [PPO](phases/09-reinforcement-learning/08-ppo/) | Build | Python | | 09 | [Reward Modeling & RLHF](phases/09-reinforcement-learning/09-reward-modeling-rlhf/) | Build | Python | | 10 | [Multi-Agent RL](phases/09-reinforcement-learning/10-multi-agent-rl/) | Build | Python | | 11 | [Sim-to-Real Transfer](phases/09-reinforcement-learning/11-sim-to-real-transfer/) | Build | Python | | 12 | [RL for Games](phases/09-reinforcement-learning/12-rl-for-games/) | Build | Python |
Phase 10 — LLMs from Scratch  24 lessons  Build, train, and understand large language models.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Tokenizers: BPE, WordPiece, SentencePiece](phases/10-llms-from-scratch/01-tokenizers/) | Build | Python, Rust | | 02 | [Building a Tokenizer from Scratch](phases/10-llms-from-scratch/02-building-a-tokenizer/) | Build | Python | | 03 | [Data Pipelines for Pre-Training](phases/10-llms-from-scratch/03-data-pipelines/) | Build | Python | | 04 | [Pre-Training a Mini GPT (124M)](phases/10-llms-from-scratch/04-pre-training-mini-gpt/) | Build | Python | | 05 | [Distributed Training, FSDP, DeepSpeed](phases/10-llms-from-scratch/05-scaling-distributed/) | Build | Python | | 06 | [Instruction Tuning — SFT](phases/10-llms-from-scratch/06-instruction-tuning-sft/) | Build | Python | | 07 | [RLHF — Reward Model + PPO](phases/10-llms-from-scratch/07-rlhf/) | Build | Python | | 08 | [DPO — Direct Preference Optimization](phases/10-llms-from-scratch/08-dpo/) | Build | Python | | 09 | [Constitutional AI & Self-Improvement](phases/10-llms-from-scratch/09-constitutional-ai-self-improvement/) | Build | Python | | 10 | [Evaluation — Benchmarks, Evals](phases/10-llms-from-scratch/10-evaluation/) | Build | Python | | 11 | [Quantization: INT8, GPTQ, AWQ, GGUF](phases/10-llms-from-scratch/11-quantization/) | Build | Python | | 12 | [Inference Optimization](phases/10-llms-from-scratch/12-inference-optimization/) | Build | Python | | 13 | [Building a Complete LLM Pipeline](phases/10-llms-from-scratch/13-building-complete-llm-pipeline/) | Build | Python | | 14 | [Open Models: Architecture Walkthroughs](phases/10-llms-from-scratch/14-open-models-architecture-walkthroughs/) | Learn | Python | | 15 | [Speculative Decoding and EAGLE-3](phases/10-llms-from-scratch/15-speculative-decoding-eagle3/) | Build | Python | | 16 | [Differential Attention (V2)](phases/10-llms-from-scratch/16-differential-attention-v2/) | Build | Python | | 17 | [Native Sparse Attention (DeepSeek NSA)](phases/10-llms-from-scratch/17-native-sparse-attention/) | Build | Python | | 18 | [Multi-Token Prediction (MTP)](phases/10-llms-from-scratch/18-multi-token-prediction/) | Build | Python | | 19 | [DualPipe Parallelism](phases/10-llms-from-scratch/19-dualpipe-parallelism/) | Learn | Python | | 20 | [DeepSeek-V3 Architecture Walkthrough](phases/10-llms-from-scratch/20-deepseek-v3-walkthrough/) | Learn | Python | | 21 | [Jamba — Hybrid SSM-Transformer](phases/10-llms-from-scratch/21-jamba-hybrid-ssm-transformer/) | Learn | Python | | 22 | [Async and Hogwild! Inference](phases/10-llms-from-scratch/22-async-hogwild-inference/) | Build | Python | | 25 | [Speculative Decoding and EAGLE](phases/10-llms-from-scratch/25-speculative-decoding/) | Build | Python | | 34 | [Gradient Checkpointing and Activation Recomputation](phases/10-llms-from-scratch/34-gradient-checkpointing/) | Build | Python |
Phase 11 — LLM Engineering  17 lessons  Put LLMs to work in production.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Prompt Engineering: Techniques & Patterns](phases/11-llm-engineering/01-prompt-engineering/) | Build | Python | | 02 | [Few-Shot, CoT, Tree-of-Thought](phases/11-llm-engineering/02-few-shot-cot/) | Build | Python | | 03 | [Structured Outputs](phases/11-llm-engineering/03-structured-outputs/) | Build | Python | | 04 | [Embeddings & Vector Representations](phases/11-llm-engineering/04-embeddings/) | Build | Python | | 05 | [Context Engineering](phases/11-llm-engineering/05-context-engineering/) | Build | Python | | 06 | [RAG: Retrieval-Augmented Generation](phases/11-llm-engineering/06-rag/) | Build | Python | | 07 | [Advanced RAG: Chunking, Reranking](phases/11-llm-engineering/07-advanced-rag/) | Build | Python | | 08 | [Fine-Tuning with LoRA & QLoRA](phases/11-llm-engineering/08-fine-tuning-lora/) | Build | Python | | 09 | [Function Calling & Tool Use](phases/11-llm-engineering/09-function-calling/) | Build | Python | | 10 | [Evaluation & Testing](phases/11-llm-engineering/10-evaluation/) | Build | Python | | 11 | [Caching, Rate Limiting & Cost](phases/11-llm-engineering/11-caching-cost/) | Build | Python | | 12 | [Guardrails & Safety](phases/11-llm-engineering/12-guardrails/) | Build | Python | | 13 | [Building a Production LLM App](phases/11-llm-engineering/13-production-app/) | Build | Python | | 14 | [Model Context Protocol (MCP)](phases/11-llm-engineering/14-model-context-protocol/) | Build | Python | | 15 | [Prompt Caching & Context Caching](phases/11-llm-engineering/15-prompt-caching/) | Build | Python | | 16 | [Agent State Machines — Graphs, Nodes, Checkpoints](phases/11-llm-engineering/16-langgraph-state-machines/) | Build | Python | | 17 | [Agent Framework Tradeoffs](phases/11-llm-engineering/17-agent-framework-tradeoffs/) | Learn | Python |
Phase 12 — Multimodal AI  25 lessons  See, hear, read, and reason across modalities — from ViT patches to computer-use agents.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Vision Transformers and the Patch-Token Primitive](phases/12-multimodal-ai/01-vision-transformer-patch-tokens/) | Learn | Python | | 02 | [CLIP and Contrastive Vision-Language Pretraining](phases/12-multimodal-ai/02-clip-contrastive-pretraining/) | Build | Python | | 03 | [BLIP-2 Q-Former as Modality Bridge](phases/12-multimodal-ai/03-blip2-qformer-bridge/) | Build | Python | | 04 | [Flamingo and Gated Cross-Attention](phases/12-multimodal-ai/04-flamingo-gated-cross-attention/) | Learn | Python | | 05 | [LLaVA and Visual Instruction Tuning](phases/12-multimodal-ai/05-llava-visual-instruction-tuning/) | Build | Python | | 06 | [Any-Resolution Vision — Patch-n'-Pack and NaFlex](phases/12-multimodal-ai/06-any-resolution-patch-n-pack/) | Build | Python | | 07 | [Open-Weight VLM Recipes: What Actually Matters](phases/12-multimodal-ai/07-open-weight-vlm-recipes/) | Learn | Python | | 08 | [LLaVA-OneVision: Single, Multi, Video](phases/12-multimodal-ai/08-llava-onevision-single-multi-video/) | Build | Python | | 09 | [Qwen-VL Family and Dynamic-FPS Video](phases/12-multimodal-ai/09-qwen-vl-family-dynamic-fps/) | Learn | Python | | 10 | [InternVL3 Native Multimodal Pretraining](phases/12-multimodal-ai/10-internvl3-native-multimodal/) | Learn | Python | | 11 | [Chameleon Early-Fusion Token-Only](phases/12-multimodal-ai/11-chameleon-early-fusion-tokens/) | Build | Python | | 12 | [Emu3 Next-Token Prediction for Generation](phases/12-multimodal-ai/12-emu3-next-token-for-generation/) | Learn | Python | | 13 | [Transfusion Autoregressive + Diffusion](phases/12-multimodal-ai/13-transfusion-autoregressive-diffusion/) | Build | Python | | 14 | [Show-o Discrete-Diffusion Unified](phases/12-multimodal-ai/14-show-o-discrete-diffusion-unified/) | Learn | Python | | 15 | [Janus-Pro Decoupled Encoders](phases/12-multimodal-ai/15-janus-pro-decoupled-encoders/) | Build | Python | | 16 | [MIO Any-to-Any Streaming](phases/12-multimodal-ai/16-mio-any-to-any-streaming/) | Learn | Python | | 17 | [Video-Language Temporal Grounding](phases/12-multimodal-ai/17-video-language-temporal-grounding/) | Build | Python | | 18 | [Long-Video at Million-Token Context](phases/12-multimodal-ai/18-long-video-million-token/) | Build | Python | | 19 | [Audio-Language Models: Whisper to AF3](phases/12-multimodal-ai/19-audio-language-whisper-to-af3/) | Build | Python | | 20 | [Omni Models: Thinker-Talker Streaming](phases/12-multimodal-ai/20-omni-models-thinker-talker/) | Build | Python | | 21 | [Embodied VLAs: RT-2, OpenVLA, π0, GR00T](phases/12-multimodal-ai/21-embodied-vlas-openvla-pi0-groot/) | Learn | Python | | 22 | [Document and Diagram Understanding](phases/12-multimodal-ai/22-document-diagram-understanding/) | Build | Python | | 23 | [ColPali Vision-Native Document RAG](phases/12-multimodal-ai/23-colpali-vision-native-rag/) | Build | Python | | 24 | [Multimodal RAG and Cross-Modal Retrieval](phases/12-multimodal-ai/24-multimodal-rag-cross-modal/) | Build | Python | | 25 | [Multimodal Agents and Computer-Use (Capstone)](phases/12-multimodal-ai/25-multimodal-agents-computer-use/) | Build | Python |
Phase 13 — Tools & Protocols  23 lessons  The interfaces between AI and the real world.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [The Tool Interface](phases/13-tools-and-protocols/01-the-tool-interface/) | Learn | Python | | 02 | [Function Calling Deep Dive](phases/13-tools-and-protocols/02-function-calling-deep-dive/) | Build | Python | | 03 | [Parallel and Streaming Tool Calls](phases/13-tools-and-protocols/03-parallel-and-streaming-tool-calls/) | Build | Python | | 04 | [Structured Output](phases/13-tools-and-protocols/04-structured-output/) | Build | Python | | 05 | [Tool Schema Design](phases/13-tools-and-protocols/05-tool-schema-design/) | Learn | Python | | 06 | [MCP Fundamentals](phases/13-tools-and-protocols/06-mcp-fundamentals/) | Learn | Python | | 07 | [Building an MCP Server](phases/13-tools-and-protocols/07-building-an-mcp-server/) | Build | Python | | 08 | [Building an MCP Client](phases/13-tools-and-protocols/08-building-an-mcp-client/) | Build | Python | | 09 | [MCP Transports](phases/13-tools-and-protocols/09-mcp-transports/) | Learn | Python | | 10 | [MCP Resources and Prompts](phases/13-tools-and-protocols/10-mcp-resources-and-prompts/) | Build | Python | | 11 | [MCP Sampling](phases/13-tools-and-protocols/11-mcp-sampling/) | Build | Python | | 12 | [MCP Roots and Elicitation](phases/13-tools-and-protocols/12-mcp-roots-and-elicitation/) | Build | Python | | 13 | [MCP Async Tasks](phases/13-tools-and-protocols/13-mcp-async-tasks/) | Build | Python | | 14 | [MCP Apps](phases/13-tools-and-protocols/14-mcp-apps/) | Build | Python | | 15 | [MCP Security I — Tool Poisoning](phases/13-tools-and-protocols/15-mcp-security-tool-poisoning/) | Learn | Python | | 16 | [MCP Security II — OAuth 2.1](phases/13-tools-and-protocols/16-mcp-security-oauth-2-1/) | Build | Python | | 17 | [MCP Gateways and Registries](phases/13-tools-and-protocols/17-mcp-gateways-and-registries/) | Learn | Python | | 18 | [MCP Auth in Production — Enrollment, JWKS Refresh, Audience Pinning](phases/13-tools-and-protocols/18-mcp-auth-production/) | Build | Python | | 19 | [A2A Protocol](phases/13-tools-and-protocols/19-a2a-protocol/) | Build | Python | | 20 | [OpenTelemetry GenAI](phases/13-tools-and-protocols/20-opentelemetry-genai/) | Build | Python | | 21 | [LLM Routing Layer](phases/13-tools-and-protocols/21-llm-routing-layer/) | Learn | Python | | 22 | [Skills and Agent SDKs](phases/13-tools-and-protocols/22-skills-and-agent-sdks/) | Learn | Python | | 23 | [Capstone — Tool Ecosystem](phases/13-tools-and-protocols/23-capstone-tool-ecosystem/) | Build | Python |
Phase 14 — Agent Engineering  42 lessons  Build agents from first principles — loop, memory, planning, frameworks, benchmarks, production, workbench.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [The Agent Loop](phases/14-agent-engineering/01-the-agent-loop/) | Build | Python | | 02 | [ReWOO and Plan-and-Execute](phases/14-agent-engineering/02-rewoo-plan-and-execute/) | Build | Python | | 03 | [Reflexion and Verbal Reinforcement Learning](phases/14-agent-engineering/03-reflexion-verbal-rl/) | Build | Python | | 04 | [Tree of Thoughts and LATS](phases/14-agent-engineering/04-tree-of-thoughts-lats/) | Build | Python | | 05 | [Self-Refine and CRITIC](phases/14-agent-engineering/05-self-refine-and-critic/) | Build | Python | | 06 | [Tool Use and Function Calling](phases/14-agent-engineering/06-tool-use-and-function-calling/) | Build | Python | | 07 | [Agent Memory — Virtual Context and Memory Paging](phases/14-agent-engineering/07-memory-virtual-context-memgpt/) | Build | Python | | 08 | [Memory Blocks and Sleep-Time Compute](phases/14-agent-engineering/08-memory-blocks-sleep-time-compute/) | Build | Python | | 09 | [Hybrid Memory — Vector + Graph + KV](phases/14-agent-engineering/09-hybrid-memory-mem0/) | Build | Python | | 10 | [Skill Libraries and Lifelong Learning (Voyager)](phases/14-agent-engineering/10-skill-libraries-voyager/) | Build | Python | | 11 | [Planning with HTN and Evolutionary Search](phases/14-agent-engineering/11-planning-htn-and-evolutionary/) | Build | Python | | 12 | [Anthropic's Workflow Patterns](phases/14-agent-engineering/12-anthropic-workflow-patterns/) | Build | Python | | 13 | [Stateful Graph Orchestration — Durable Execution and Checkpoints](phases/14-agent-engineering/13-langgraph-stateful-graphs/) | Build | Python | | 14 | [The Actor Model for Agents](phases/14-agent-engineering/14-autogen-actor-model/) | Build | Python | | 15 | [Role-Based Agent Teams — Roles, Tasks, Processes](phases/14-agent-engineering/15-crewai-role-based-crews/) | Build | Python | | 16 | [OpenAI Agents SDK — Handoffs, Guardrails, Tracing](phases/14-agent-engineering/16-openai-agents-sdk/) | Build | Python | | 17 | [The Harness as a Library — Subagents and Session Store](phases/14-agent-engineering/17-claude-agent-sdk/) | Build | Python | | 18 | [Production Agent Runtimes](phases/14-agent-engineering/18-agno-and-mastra-runtimes/) | Learn | Python | | 19 | [Benchmarks — SWE-bench, GAIA, AgentBench](phases/14-agent-engineering/19-benchmarks-swebench-gaia/) | Learn | Python | | 20 | [Benchmarks — WebArena and OSWorld](phases/14-agent-engineering/20-benchmarks-webarena-osworld/) | Learn | Python | | 21 | [Computer Use — Claude, OpenAI CUA, Gemini](phases/14-agent-engineering/21-computer-use-agents/) | Build | Python | | 22 | [Voice Agents — Pipecat and LiveKit](phases/14-agent-engineering/22-voice-agents-pipecat-livekit/) | Build | Python | | 23 | [OpenTelemetry GenAI Semantic Conventions](phases/14-agent-engineering/23-otel-genai-conventions/) | Build | Python | | 24 | [Agent Observability — Langfuse, Phoenix, Opik](phases/14-agent-engineering/24-agent-observability-platforms/) | Learn | Python | | 25 | [Multi-Agent Debate and Collaboration](phases/14-agent-engineering/25-multi-agent-debate/) | Build | Python | | 26 | [Failure Modes — Why Agents Break](phases/14-agent-engineering/26-failure-modes-agentic/) | Build | Python | | 27 | [Prompt Injection and the PVE Defense](phases/14-agent-engineering/27-prompt-injection-defense/) | Build | Python | | 28 | [Orchestration Patterns — Supervisor, Swarm, Hierarchical](phases/14-agent-engineering/28-orchestration-patterns/) | Build | Python | | 29 | [Production Runtimes — Queue, Event, Cron](phases/14-agent-engineering/29-production-runtimes/) | Learn | Python | | 30 | [Eval-Driven Agent Development](phases/14-agent-engineering/30-eval-driven-agent-development/) | Build | Python | | 31 | [Agent Workbench: Why Capable Models Still Fail](phases/14-agent-engineering/31-agent-workbench-why-models-fail/) | Learn | Python | | 32 | [The Minimal Agent Workbench](phases/14-agent-engineering/32-minimal-agent-workbench/) | Build | Python | | 33 | [Agent Instructions as Executable Constraints](phases/14-agent-engineering/33-instructions-as-executable-constraints/) | Build | Python | | 34 | [Repo Memory and Durable State](phases/14-agent-engineering/34-repo-memory-and-state/) | Build | Python | | 35 | [Initialization Scripts for Agents](phases/14-agent-engineering/35-initialization-scripts/) | Build | Python | | 36 | [Scope Contracts and Task Boundaries](phases/14-agent-engineering/36-scope-contracts/) | Build | Python | | 37 | [Runtime Feedback Loops](phases/14-agent-engineering/37-runtime-feedback-loops/) | Build | Python | | 38 | [Verification Gates](phases/14-agent-engineering/38-verification-gates/) | Build | Python | | 39 | [Reviewer Agent: Separate Builder from Marker](phases/14-agent-engineering/39-reviewer-agent/) | Build | Python | | 40 | [Multi-Session Handoff](phases/14-agent-engineering/40-multi-session-handoff/) | Build | Python | | 41 | [The Workbench on a Real Repo](phases/14-agent-engineering/41-workbench-for-real-repos/) | Build | Python | | 42 | [Capstone: Ship a Reusable Agent Workbench Pack](phases/14-agent-engineering/42-agent-workbench-capstone/) | Build | Python | Each Phase 14 workbench lesson (31-42) ships a `mission.md` briefing the agent before it opens the full lesson docs.
Phase 15 — Autonomous Systems  22 lessons  Long-horizon agents, self-improvement, and the 2026 safety stack.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [From Chatbots to Long-Horizon Agents (METR)](phases/15-autonomous-systems/01-long-horizon-agents/) | Learn | Python | | 02 | [STaR, V-STaR, Quiet-STaR: Self-Taught Reasoning](phases/15-autonomous-systems/02-star-family-reasoning/) | Learn | Python | | 03 | [AlphaEvolve: Evolutionary Coding Agents](phases/15-autonomous-systems/03-alphaevolve-evolutionary-coding/) | Learn | Python | | 04 | [Darwin Gödel Machine: Self-Modifying Agents](phases/15-autonomous-systems/04-darwin-godel-machine/) | Learn | Python | | 05 | [AI Scientist v2: Workshop-Level Research](phases/15-autonomous-systems/05-ai-scientist-v2/) | Learn | Python | | 06 | [Automated Alignment Research (Anthropic AAR)](phases/15-autonomous-systems/06-automated-alignment-research/) | Learn | Python | | 07 | [Recursive Self-Improvement: Capability vs Alignment](phases/15-autonomous-systems/07-recursive-self-improvement/) | Learn | Python | | 08 | [Bounded Self-Improvement Designs](phases/15-autonomous-systems/08-bounded-self-improvement/) | Learn | Python | | 09 | [Autonomous Coding Agent Landscape (SWE-bench, CodeAct)](phases/15-autonomous-systems/09-coding-agent-landscape/) | Learn | Python | | 10 | [Permission Modes for Autonomous Agents](phases/15-autonomous-systems/10-claude-code-permission-modes/) | Learn | Python | | 11 | [Browser Agents and Indirect Prompt Injection](phases/15-autonomous-systems/11-browser-agents/) | Learn | Python | | 12 | [Durable Execution for Long-Running Agents](phases/15-autonomous-systems/12-durable-execution/) | Learn | Python | | 13 | [Action Budgets, Iteration Caps, Cost Governors](phases/15-autonomous-systems/13-cost-governors/) | Learn | Python | | 14 | [Kill Switches, Circuit Breakers, Canary Tokens](phases/15-autonomous-systems/14-kill-switches-canaries/) | Learn | Python | | 15 | [HITL: Propose-Then-Commit](phases/15-autonomous-systems/15-propose-then-commit/) | Learn | Python | | 16 | [Checkpoints and Rollback](phases/15-autonomous-systems/16-checkpoints-rollback/) | Learn | Python | | 17 | [Constitutional AI and Rule Overrides](phases/15-autonomous-systems/17-constitutional-ai/) | Learn | Python | | 18 | [Llama Guard and Input/Output Classification](phases/15-autonomous-systems/18-llama-guard/) | Learn | Python | | 19 | [Anthropic Responsible Scaling Policy v3.0](phases/15-autonomous-systems/19-anthropic-rsp/) | Learn | Python | | 20 | [OpenAI Preparedness Framework and DeepMind FSF](phases/15-autonomous-systems/20-openai-preparedness-deepmind-fsf/) | Learn | Python | | 21 | [METR Time Horizons and External Evaluation](phases/15-autonomous-systems/21-metr-external-evaluation/) | Learn | Python | | 22 | [CAIS, CAISI, and Societal-Scale Risk](phases/15-autonomous-systems/22-cais-caisi-societal-risk/) | Learn | Python |
Phase 16 — Multi-Agent & Swarms  25 lessons  Coordination, emergence, and collective intelligence.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Why Multi-Agent](phases/16-multi-agent-and-swarms/01-why-multi-agent/) | Learn | TypeScript | | 02 | [FIPA-ACL Heritage and Speech Acts](phases/16-multi-agent-and-swarms/02-fipa-acl-heritage/) | Learn | Python | | 03 | [Communication Protocols](phases/16-multi-agent-and-swarms/03-communication-protocols/) | Build | TypeScript | | 04 | [The Multi-Agent Primitive Model](phases/16-multi-agent-and-swarms/04-primitive-model/) | Learn | Python | | 05 | [Supervisor / Orchestrator-Worker Pattern](phases/16-multi-agent-and-swarms/05-supervisor-orchestrator-pattern/) | Build | Python | | 06 | [Hierarchical Architecture and Decomposition Drift](phases/16-multi-agent-and-swarms/06-hierarchical-architecture/) | Learn | Python | | 07 | [Society of Mind and Multi-Agent Debate](phases/16-multi-agent-and-swarms/07-society-of-mind-debate/) | Build | Python | | 08 | [Role Specialization — Planner / Critic / Executor / Verifier](phases/16-multi-agent-and-swarms/08-role-specialization/) | Build | Python | | 09 | [Parallel Swarm and Networked Architectures](phases/16-multi-agent-and-swarms/09-parallel-swarm-networks/) | Build | Python | | 10 | [Group Chat and Speaker Selection](phases/16-multi-agent-and-swarms/10-group-chat-speaker-selection/) | Build | Python | | 11 | [Handoffs and Routines (Stateless Orchestration)](phases/16-multi-agent-and-swarms/11-handoffs-and-routines/) | Build | Python | | 12 | [A2A — The Agent-to-Agent Protocol](phases/16-multi-agent-and-swarms/12-a2a-protocol/) | Build | Python | | 13 | [Shared Memory and Blackboard Patterns](phases/16-multi-agent-and-swarms/13-shared-memory-blackboard/) | Build | Python | | 14 | [Consensus and Byzantine Fault Tolerance](phases/16-multi-agent-and-swarms/14-consensus-and-bft/) | Build | Python | | 15 | [Voting, Self-Consistency, and Debate Topology](phases/16-multi-agent-and-swarms/15-voting-debate-topology/) | Build | Python | | 16 | [Negotiation and Bargaining](phases/16-multi-agent-and-swarms/16-negotiation-bargaining/) | Build | Python | | 17 | [Generative Agents and Emergent Simulation](phases/16-multi-agent-and-swarms/17-generative-agents-simulation/) | Build | Python | | 18 | [Theory of Mind and Emergent Coordination](phases/16-multi-agent-and-swarms/18-theory-of-mind-coordination/) | Build | Python | | 19 | [Swarm Optimization (PSO, ACO)](phases/16-multi-agent-and-swarms/19-swarm-optimization-pso-aco/) | Build | Python | | 20 | [MARL — MADDPG, QMIX, MAPPO](phases/16-multi-agent-and-swarms/20-marl-maddpg-qmix-mappo/) | Learn | Python | | 21 | [Agent Economies, Token Incentives, Reputation](phases/16-multi-agent-and-swarms/21-agent-economies/) | Learn | Python | | 22 | [Production Scaling — Queues, Checkpoints, Durability](phases/16-multi-agent-and-swarms/22-production-scaling-queues-checkpoints/) | Build | Python | | 23 | [Failure Modes — MAST, Groupthink, Monoculture](phases/16-multi-agent-and-swarms/23-failure-modes-mast-groupthink/) | Learn | Python | | 24 | [Evaluation and Coordination Benchmarks](phases/16-multi-agent-and-swarms/24-evaluation-coordination-benchmarks/) | Learn | Python | | 25 | [Case Studies and 2026 State of the Art](phases/16-multi-agent-and-swarms/25-case-studies-2026-sota/) | Learn | Python |
Phase 17 — Infrastructure & Production  28 lessons  Ship AI to the real world.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Managed LLM Platforms — Bedrock, Azure OpenAI, Vertex AI](phases/17-infrastructure-and-production/01-managed-llm-platforms/) | Learn | Python | | 02 | [Inference Platform Economics — Fireworks, Together, Baseten, Modal](phases/17-infrastructure-and-production/02-inference-platform-economics/) | Learn | Python | | 03 | [GPU Autoscaling on Kubernetes — Karpenter, KAI Scheduler](phases/17-infrastructure-and-production/03-gpu-autoscaling-kubernetes/) | Learn | Python | | 04 | [Serving Engine Internals — PagedAttention, Continuous Batching, Chunked Prefill](phases/17-infrastructure-and-production/04-vllm-serving-internals/) | Learn | Python | | 05 | [EAGLE-3 Speculative Decoding in Production](phases/17-infrastructure-and-production/05-eagle3-speculative-decoding/) | Learn | Python | | 06 | [Prefix-Cache Serving — RadixAttention and KV Reuse](phases/17-infrastructure-and-production/06-sglang-radixattention/) | Learn | Python | | 07 | [Hardware-Specialized Inference Compilation — FP8 and NVFP4 on Blackwell](phases/17-infrastructure-and-production/07-tensorrt-llm-blackwell/) | Learn | Python | | 08 | [Inference Metrics — TTFT, TPOT, ITL, Goodput, P99](phases/17-infrastructure-and-production/08-inference-metrics-goodput/) | Learn | Python | | 09 | [Production Quantization — AWQ, GPTQ, GGUF, FP8, NVFP4](phases/17-infrastructure-and-production/09-production-quantization/) | Learn | Python | | 10 | [Cold Start Mitigation for Serverless LLMs](phases/17-infrastructure-and-production/10-cold-start-mitigation/) | Learn | Python | | 11 | [Multi-Region LLM Serving and KV Cache Locality](phases/17-infrastructure-and-production/11-multi-region-kv-locality/) | Learn | Python | | 12 | [Edge Inference — ANE, Hexagon, WebGPU, Jetson](phases/17-infrastructure-and-production/12-edge-inference/) | Learn | Python | | 13 | [LLM Observability Stack Selection](phases/17-infrastructure-and-production/13-llm-observability/) | Learn | Python | | 14 | [Prompt Caching and Semantic Caching Economics](phases/17-infrastructure-and-production/14-prompt-semantic-caching/) | Learn | Python | | 15 | [Batch APIs — the 50% Discount as Industry Standard](phases/17-infrastructure-and-production/15-batch-apis/) | Learn | Python | | 16 | [Model Routing as a Cost-Reduction Primitive](phases/17-infrastructure-and-production/16-model-routing/) | Learn | Python | | 17 | [Disaggregated Prefill/Decode — NVIDIA Dynamo and llm-d](phases/17-infrastructure-and-production/17-disaggregated-prefill-decode/) | Learn | Python | | 18 | [Production Serving Stack — KV Offloading and Cache-Aware Routing](phases/17-infrastructure-and-production/18-vllm-production-stack-lmcache/) | Learn | Python | | 19 | [AI Gateways — LiteLLM, Portkey, Kong, Bifrost](phases/17-infrastructure-and-production/19-ai-gateways/) | Learn | Python | | 20 | [Shadow, Canary, and Progressive Deployment](phases/17-infrastructure-and-production/20-shadow-canary-progressive/) | Learn | Python | | 21 | [A/B Testing LLM Features — GrowthBook and Statsig](phases/17-infrastructure-and-production/21-ab-testing-llm-features/) | Learn | Python | | 22 | [Load Testing LLM APIs — k6, LLMPerf, GenAI-Perf](phases/17-infrastructure-and-production/22-load-testing-llm-apis/) | Build | Python | | 23 | [SRE for AI — Multi-Agent Incident Response](phases/17-infrastructure-and-production/23-sre-for-ai/) | Learn | Python | | 24 | [Chaos Engineering for LLM Production](phases/17-infrastructure-and-production/24-chaos-engineering-llm/) | Learn | Python | | 25 | [Security — Secrets, PII Scrubbing, Audit Logs](phases/17-infrastructure-and-production/25-security-secrets-audit/) | Learn | Python | | 26 | [Compliance — SOC 2, HIPAA, GDPR, EU AI Act, ISO 42001](phases/17-infrastructure-and-production/26-compliance-frameworks/) | Learn | Python | | 27 | [FinOps for LLMs — Unit Economics and Multi-Tenant Attribution](phases/17-infrastructure-and-production/27-finops-llms/) | Learn | Python | | 28 | [Self-Hosted Serving Selection — Matching Engine to Hardware and Scale](phases/17-infrastructure-and-production/28-self-hosted-serving-selection/) | Learn | Python |
Phase 18 — Ethics, Safety & Alignment  30 lessons  Build AI that helps humanity. Not optional.
| # | Lesson | Type | Lang | |:---:|--------|:----:|------| | 01 | [Instruction-Following as Alignment Signal](phases/18-ethics-safety-alignment/01-instruction-following-alignment-signal/) | Learn | Python | | 02 | [Reward Hacking & Goodhart's Law](phases/18-ethics-safety-alignment/02-reward-hacking-goodhart/) | Learn | Python | | 03 | [Direct Preference Optimization Family](phases/18-ethics-safety-alignment/03-direct-preference-optimization-family/) | Learn | Python | | 04 | [Sycophancy as RLHF Amplification](phases/18-ethics-safety-alignment/04-sycophancy-rlhf-amplification/) | Learn | Python | | 05 | [Constitutional AI & RLAIF](phases/18-ethics-safety-alignment/05-constitutional-ai-rlaif/) | Learn | Python | | 06 | [Mesa-Optimization & Deceptive Alignment](phases/18-ethics-safety-alignment/06-mesa-optimization-deceptive-alignment/) | Learn | Python | | 07 | [Sleeper Agents — Persistent Deception](phases/18-ethics-safety-alignment/07-sleeper-agents-persistent-deception/) | Learn | Python | | 08 | [In-Context Scheming in Frontier Models](phases/18-ethics-safety-alignment/08-in-context-scheming-frontier-models/) | Learn | Python | | 09 | [Alignment Faking](phases/18-ethics-safety-alignment/09-alignment-faking/) | Learn | Python | | 10 | [AI Control — Safety Despite Subversion](phases/18-ethics-safety-alignment/10-ai-control-subversion/) | Learn | Python | | 11 | [Scalable Oversight & Weak-to-Strong](phases/18-ethics-safety-alignment/11-scalable-oversight-weak-to-strong/) | Learn | Python | | 12 | [Red-Teaming: PAIR & Automated Attacks](phases/18-ethics-safety-alignment/12-red-teaming-pair-automated-attacks/) | Build | Python | | 13 | [Many-Shot Jailbreaking](phases/18-ethics-safety-alignment/13-many-shot-jailbreaking/) | Learn | Python | | 14 | [ASCII Art & Visual Jailbreaks](phases/18-ethics-safety-alignment/14-ascii-art-visual-jailbreaks/) | Build | Python | | 15 | [Indirect Prompt Injection](phases/18-ethics-safety-alignment/15-indirect-prompt-injection/) | Build | Python | | 16 | [Red-Team Tooling: Garak, Llama Guard, PyRIT](phases/18-ethics-safety-alignment/16-red-team-tooling-garak-llamaguard-pyrit/) | Build | Python | | 17 | [WMDP & Dual-Use Capability Evaluation](phases/18-ethics-safety-alignment/17-wmdp-dual-use-evaluation/) | Learn | Python | | 18 | [Frontier Safety Frameworks — RSP, PF, FSF](phases/18-ethics-safety-alignment/18-frontier-safety-frameworks-rsp-pf-fsf/) | Learn | Python | | 19 | [Model Welfare Research](phases/18-ethics-safety-alignment/19-model-welfare-research/) | Learn | Python | | 20 | [Bias & Representational Harm](phases/18-ethics-safety-alignment/20-bias-representational-harm/) | Build | Python | | 21 | [Fairness Criteria: Group, Individual, Counterfactual](phases/18-ethics-safety-alignment/21-fairness-criteria-group-individual-counterfactual/) | Learn | Python | | 22 | [Differential Privacy for LLMs](phases/18-ethics-safety-alignment/22-differential-privacy-for-llms/) | Build | Python | | 23 | [Watermarking: SynthID, Stable Signature, C2PA](phases/18-ethics-safety-alignment/23-watermarking-synthid-stable-signature-c2pa/) | Build | Python | | 24 | [Regulatory Frameworks: EU, US, UK, Korea](phases/18-ethics-safety-alignment/24-regulatory-frameworks-eu-us-uk-korea/) | Learn | Python | | 25 | [EchoLeak & CVEs for AI](phases/18-ethics-safety-alignment/25-echoleak-cves-for-ai/) | Learn | Python | | 26 | [Model, System & Dataset Cards](phases/18-ethics-safety-alignment/26-model-system-dataset-cards/) | Build | Python | | 27 | [Data Provenance & Training-Data Governance](phases/18-ethics-safety-alignment/27-data-provenance-training-governance/) | Learn | Python | | 28 | [Alignment Research Ecosystem: MATS, Redwood, Apollo, METR](phases/18-ethics-safety-alignment/28-alignment-research-ecosystem/) | Learn | Python | | 29 | [Moderation Systems: OpenAI, Perspective, Llama Guard](phases/18-ethics-safety-alignment/29-moderation-systems-openai-perspective-llamaguard/) | Build | Python | | 30 | [Dual-Use Risk: Cyber, Bio, Chem, Nuclear](phases/18-ethics-safety-alignment/30-dual-use-risk-cyber-bio-chem-nuclear/) | Learn | Python |
Phase 19 — Capstone Projects  85 lessons  17 end-to-end products + 9 deep-build tracks. 20-40 hours per project; 4-12 lessons per track.
| # | Project | Combines | Lang | |:---:|---------|----------|------| | 01 | [Terminal-Native Coding Agent](phases/19-capstone-projects/01-terminal-native-coding-agent/) | P0 P5 P7 P10 P11 P13 P14 P15 P17 P18 | Python | | 02 | [RAG over Codebase (Cross-Repo Semantic Search)](phases/19-capstone-projects/02-rag-over-codebase/) | P5 P7 P11 P13 P17 | Python | | 03 | [Real-Time Voice Assistant (ASR → LLM → TTS)](phases/19-capstone-projects/03-realtime-voice-assistant/) | P6 P7 P11 P13 P14 P17 | Python | | 04 | [Multimodal Document QA (Vision-First)](phases/19-capstone-projects/04-multimodal-document-qa/) | P4 P5 P7 P11 P12 P17 | Python | | 05 | [Autonomous Research Agent (AI-Scientist Class)](phases/19-capstone-projects/05-autonomous-research-agent/) | P0 P2 P3 P7 P10 P14 P15 P16 P18 | Python | | 06 | [DevOps Troubleshooting Agent for Kubernetes](phases/19-capstone-projects/06-devops-troubleshooting-agent/) | P11 P13 P14 P15 P17 P18 | Python | | 07 | [End-to-End Fine-Tuning Pipeline](phases/19-capstone-projects/07-end-to-end-fine-tuning-pipeline/) | P2 P3 P7 P10 P11 P17 P18 | Python | | 08 | [Production RAG Chatbot (Regulated Vertical)](phases/19-capstone-projects/08-production-rag-chatbot/) | P5 P7 P11 P12 P17 P18 | Python | | 09 | [Code Migration Agent (Repo-Level Upgrade)](phases/19-capstone-projects/09-code-migration-agent/) | P5 P7 P11 P13 P14 P15 P17 | Python | | 10 | [Multi-Agent Software Engineering Team](phases/19-capstone-projects/10-multi-agent-software-team/) | P11 P13 P14 P15 P16 P17 | Python | | 11 | [LLM Observability & Eval Dashboard](phases/19-capstone-projects/11-llm-observability-dashboard/) | P11 P13 P17 P18 | Python | | 12 | [Video Understanding Pipeline (Scene → QA)](phases/19-capstone-projects/12-video-understanding-pipeline/) | P4 P6 P7 P11 P12 P17 | Python | | 13 | [MCP Server with Registry and Governance](phases/19-capstone-projects/13-mcp-server-with-registry/) | P11 P13 P14 P17 P18 | Python | | 14 | [Speculative-Decoding Inference Server](phases/19-capstone-projects/14-speculative-decoding-server/) | P3 P7 P10 P17 | Python | | 15 | [Constitutional Safety Harness + Red-Team Range](phases/19-capstone-projects/15-constitutional-safety-harness/) | P10 P11 P13 P14 P18 | Python | | 16 | [GitHub Issue-to-PR Autonomous Agent](phases/19-capstone-projects/16-github-issue-to-pr-agent/) | P11 P13 P14 P15 P17 | Python | | 17 | [Personal AI Tutor (Adaptive, Multimodal)](phases/19-capstone-projects/17-personal-ai-tutor/) | P5 P6 P11 P12 P14 P17 P18 | Python | **Deep-build tracks** — multi-lesson series that build a complete subsystem from scratch. | # | Project | Combines | Lang | |:---:|---------|----------|------| | 20 | [Agent Harness Loop Contract](phases/19-capstone-projects/20-agent-harness-loop-contract/) | A. Agent harness | Python | | 21 | [Tool Registry with Schema Validation](phases/19-capstone-projects/21-tool-registry-schema-validation/) | A. Agent harness | Python | | 22 | [JSON-RPC 2.0 Over Newline-Delimited Stdio](phases/19-capstone-projects/22-jsonrpc-stdio-transport/) | A. Agent harness | Python | | 23 | [Function Call Dispatcher](phases/19-capstone-projects/23-function-call-dispatcher/) | A. Agent harness | Python | | 24 | [Plan-Execute Control Flow](phases/19-capstone-projects/24-plan-execute-control-flow/) | A. Agent harness | Python | | 25 | [Verification Gates and Observation Budget](phases/19-capstone-projects/25-verification-gates-observation-budget/) | A. Agent harness | Python | | 26 | [Sandbox Runner with Denylist and Path Jail](phases/19-capstone-projects/26-sandbox-runner-denylist/) | A. Agent harness | Python | | 27 | [Eval Harness with Fixture Tasks](phases/19-capstone-projects/27-eval-harness-fixture-tasks/) | A. Agent harness | Python | | 28 | [Observability with OTel GenAI Spans and Prometheus Metrics](phases/19-capstone-projects/28-observability-otel-traces/) | A. Agent harness | Python | | 29 | [End-to-End Coding Agent on the Harness](phases/19-capstone-projects/29-end-to-end-coding-task-demo/) | A. Agent harness | Python | | 30 | [BPE Tokenizer From Scratch](phases/19-capstone-projects/30-bpe-tokenizer-from-scratch/) | B. NLP LLM | Python | | 31 | [Tokenized Dataset with Sliding Window](phases/19-capstone-projects/31-tokenized-dataset-sliding-window/) | B. NLP LLM | Python | | 32 | [Token and Positional Embeddings](phases/19-capstone-projects/32-token-positional-embeddings/) | B. NLP LLM | Python | | 33 | [Multi-Head Self-Attention](phases/19-capstone-projects/33-multihead-self-attention/) | B. NLP LLM | Python | | 34 | [Transformer Block from Scratch](phases/19-capstone-projects/34-transformer-block/) | B. NLP LLM | Python | | 35 | [GPT Model Assembly](phases/19-capstone-projects/35-gpt-model-assembly/) | B. NLP LLM | Python | | 36 | [Training Loop and Evaluation](phases/19-capstone-projects/36-training-loop-eval/) | B. NLP LLM | Python | | 37 | [Loading Pretrained Weights](phases/19-capstone-projects/37-loading-pretrained-weights/) | B. NLP LLM | Python | | 38 | [Classifier Fine-Tuning by Head Swap](phases/19-capstone-projects/38-classifier-finetuning/) | B. NLP LLM | Python | | 39 | [Instruction Tuning by Supervised Fine-Tuning](phases/19-capstone-projects/39-instruction-tuning-sft/) | B. NLP LLM | Python | | 40 | [Direct Preference Optimization from Scratch](phases/19-capstone-projects/40-dpo-from-scratch/) | B. NLP LLM | Python | | 41 | [Full Evaluation Pipeline](phases/19-capstone-projects/41-eval-pipeline/) | B. NLP LLM | Python | | 42 | [Large Corpus Downloader](phases/19-capstone-projects/42-large-corpus-downloader/) | C. Train end-to-end | Python | | 43 | [HDF5 Tokenized Corpus](phases/19-capstone-projects/43-hdf5-tokenized-corpus/) | C. Train end-to-end | Python | | 44 | [Cosine LR with Linear Warmup](phases/19-capstone-projects/44-cosine-lr-warmup/) | C. Train end-to-end | Python | | 45 | [Gradient Clipping and Mixed Precision](phases/19-capstone-projects/45-gradient-clipping-amp/) | C. Train end-to-end | Python | | 46 | [Gradient Accumulation](phases/19-capstone-projects/46-gradient-accumulation/) | C. Train end-to-end | Python | | 47 | [Checkpoint Save and Resume](phases/19-capstone-projects/47-checkpoint-save-resume/) | C. Train end-to-end | Python | | 48 | [Distributed Data Parallel and FSDP from Scratch](phases/19-capstone-projects/48-distributed-fsdp-ddp/) | C. Train end-to-end | Python | | 49 | [Language Model Evaluation Harness](phases/19-capstone-projects/49-lm-eval-harness/) | C. Train end-to-end | Python | | 50 | [Hypothesis Generator](phases/19-capstone-projects/50-hypothesis-generator/) | D. Auto research | Python | | 51 | [Literature Retrieval](phases/19-capstone-projects/51-literature-retrieval/) | D. Auto research | Python | | 52 | [Experiment Runner](phases/19-capstone-projects/52-experiment-runner/) | D. Auto research | Python | | 53 | [Result Evaluator](phases/19-capstone-projects/53-result-evaluator/) | D. Auto research | Python | | 54 | [Paper Writer](phases/19-capstone-projects/54-paper-writer/) | D. Auto research | Python | | 55 | [Critic Loop](phases/19-capstone-projects/55-critic-loop/) | D. Auto research | Python | | 56 | [Iteration Scheduler](phases/19-capstone-projects/56-iteration-scheduler/) | D. Auto research | Python | | 57 | [End-to-End Research Demo](phases/19-capstone-projects/57-end-to-end-research-demo/) | D. Auto research | Python | | 58 | [Vision Encoder Patches](phases/19-capstone-projects/58-vision-encoder-patches/) | E. Multimodal VLM | Python | | 59 | [Vision Transformer Encoder](phases/19-capstone-projects/59-vit-transformer/) | E. Multimodal VLM | Python | | 60 | [Projection Layer for Modality Alignment](phases/19-capstone-projects/60-projection-layer-modality-align/) | E. Multimodal VLM | Python | | 61 | [Cross-Attention Fusion](phases/19-capstone-projects/61-cross-attention-fusion/) | E. Multimodal VLM | Python | | 62 | [Vision-Language Pretraining](phases/19-capstone-projects/62-vision-language-pretraining/) | E. Multimodal VLM | Python | | 63 | [Multimodal Evaluation](phases/19-capstone-projects/63-multimodal-eval/) | E. Multimodal VLM | Python | | 64 | [Chunking Strategies, Compared](phases/19-capstone-projects/64-chunking-strategies-advanced/) | F. Advanced RAG | Python | | 65 | [Hybrid Retrieval with BM25 and Dense Embeddings](phases/19-capstone-projects/65-hybrid-retrieval-bm25-dense/) | F. Advanced RAG | Python | | 66 | [Cross-Encoder Reranker](phases/19-capstone-projects/66-reranker-cross-encoder/) | F. Advanced RAG | Python | | 67 | [Query Rewriting: HyDE, Multi-Query, and Decomposition](phases/19-capstone-projects/67-query-rewriting-hyde/) | F. Advanced RAG | Python | | 68 | [RAG Evaluation: Precision, Recall, MRR, nDCG, Faithfulness, Answer Relevance](phases/19-capstone-projects/68-rag-eval-precision-recall/) | F. Advanced RAG | Python | | 69 | [End-to-End RAG System](phases/19-capstone-projects/69-end-to-end-rag-system/) | F. Advanced RAG | Python | | 70 | [Task Spec Format](phases/19-capstone-projects/70-task-spec-format/) | G. Eval framework | Python | | 71 | [Classical Metrics](phases/19-capstone-projects/71-classical-metrics/) | G. Eval framework | Python | | 72 | [Code Exec Metric](phases/19-capstone-projects/72-code-exec-metric/) | G. Eval framework | Python | | 73 | [Perplexity and Calibration](phases/19-capstone-projects/73-perplexity-calibration/) | G. Eval framework | Python | | 74 | [Leaderboard Aggregation](phases/19-capstone-projects/74-leaderboard-aggregation/) | G. Eval framework | Python | | 75 | [End-to-End Eval Runner](phases/19-capstone-projects/75-end-to-end-eval-runner/) | G. Eval framework | Python | | 76 | [Collective Ops From Scratch](phases/19-capstone-projects/76-collective-ops-from-scratch/) | H. Distributed train | Python | | 77 | [Data Parallel DDP From Scratch](phases/19-capstone-projects/77-data-parallel-ddp/) | H. Distributed train | Python | | 78 | [ZeRO Optimizer State Sharding](phases/19-capstone-projects/78-zero-parameter-sharding/) | H. Distributed train | Python | | 79 | [Pipeline Parallel and Bubble Analysis](phases/19-capstone-projects/79-pipeline-parallel/) | H. Distributed train | Python | | 80 | [Sharded Checkpoint and Atomic Resume](phases/19-capstone-projects/80-checkpoint-sharded-resume/) | H. Distributed train | Python | | 81 | [End-to-End Distributed Training](phases/19-capstone-projects/81-end-to-end-distributed-train/) | H. Distributed train | Python | | 82 | [Jailbreak Taxonomy](phases/19-capstone-projects/82-jailbreak-taxonomy/) | I. Safety harness | Python | | 83 | [Prompt Injection Detector](phases/19-capstone-projects/83-prompt-injection-detector/) | I. Safety harness | Python | | 84 | [Refusal Evaluation](phases/19-capstone-projects/84-refusal-evaluation/) | I. Safety harness | Python | | 85 | [Content Classifier Integration](phases/19-capstone-projects/85-content-classifier-integration/) | I. Safety harness | Python | | 86 | [Constitutional Rules Engine](phases/19-capstone-projects/86-constitutional-rules-engine/) | I. Safety harness | Python, YAML | | 87 | [End-to-End Safety Gate](phases/19-capstone-projects/87-end-to-end-safety-gate/) | I. Safety harness | Python |
```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## The toolkit Every lesson produces a reusable artifact. By the end you have: ```text outputs/ ├── prompts/ prompt templates for every AI task └── 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/`](skills/) and install into any agent with one command — 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 # every skill, default --layout skills (nested) python3 scripts/install_skills.py --layout skills # same as above, explicit python3 scripts/install_skills.py --type all # skills + prompts + agents python3 scripts/install_skills.py --phase 14 # one phase only python3 scripts/install_skills.py --tag rag # filter by tag python3 scripts/install_skills.py --layout flat # flat files python3 scripts/install_skills.py --dry-run # preview without writing python3 scripts/install_skills.py --force # overwrite existing files ``` `` 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` | `//SKILL.md` (nested convention, supported by Claude / Cursor / Codex / OpenClaw / Hermes) | | `by-phase` | `/phase-NN/.md` | | `flat` | `/.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 /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 — 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 `. 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` — domains that aggressively block automated HEAD/GET). ## Where to start | Background | Start at | Estimated time | |---|---|---| | New to programming and AI | Phase 0 — Setup | ~306 hours | | Know Python, new to ML | Phase 1 — Math Foundations | ~270 hours | | Know ML, new to deep learning | Phase 3 — Deep Learning Core | ~200 hours | | Know deep learning, want LLMs and agents | Phase 10 — LLMs from Scratch | ~100 hours | | Senior engineer, only want agent engineering | Phase 14 — Agent Engineering | ~60 hours | ```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## Why this matters now
FIG_003 · A
THE INDUSTRY SIGNAL		 FIG_003 · B
FOUNDATIONAL PAPERS COVERED
> *"The hottest new programming language is English."*
> — **Andrej Karpathy** ([tweet](https://x.com/karpathy/status/1617979122625712128)) > > *"Software engineering is being remade in front of our eyes."*
> — **Boris Cherny**, creator of Claude Code > > *"Models will keep getting better. The skill that compounds is **knowing what to build**."*
> — Industry consensus, 2026 		- *Attention Is All You Need* — Vaswani et al., 2017 → [Phase 7](#phase-7) - *Language Models are Few-Shot Learners* (GPT-3) → [Phase 10](#phase-10) - *Denoising Diffusion Probabilistic Models* → [Phase 8](#phase-8) - *InstructGPT / RLHF* → [Phase 10](#phase-10) - *Direct Preference Optimization* → [Phase 10](#phase-10) - *Chain-of-Thought Prompting* → [Phase 11](#phase-11) - *ReAct: Reasoning + Acting in LLMs* → [Phase 14](#phase-14) - *Model Context Protocol* — Anthropic → [Phase 13](#phase-13)
```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## Contributing | Goal | Read | |---|---| | Contribute a lesson or fix | [CONTRIBUTING.md](CONTRIBUTING.md) | | Fork for your team or school | [FORKING.md](FORKING.md) | | Lesson template | [LESSON_TEMPLATE.md](LESSON_TEMPLATE.md) | | Track progress | [ROADMAP.md](ROADMAP.md) | | Glossary | [glossary/terms.md](glossary/terms.md) | | Code of conduct | [CODE_OF_CONDUCT.md](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 (L001–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 ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## Sponsor the work Free, MIT-licensed, 503 lessons. The curriculum is maintained on sponsorship alone. Cash only. **Reach (verified 2026-05-14):** 55,593 monthly visitors · 90,709 page views · 7.5K stars · Twitter/X is the #1 acquisition channel.

Vercel OSS Program **Current sponsors:** [CodeRabbit](https://coderabbit.link/rohit-ghumare) · [iii](https://iii.dev?utm_source=ai-engineering-from-scratch&utm_medium=readme&utm_campaign=sponsor) | 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](SPONSORS.md). Sign up via [GitHub Sponsors](https://github.com/sponsors/rohitg00). ```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## Star history Star history If this manual helped you, star the repo. It keeps the project alive. ## License MIT. Use it however you want — fork it, teach it, sell it, ship it. Attribution appreciated, not required. Maintained by [Rohit Ghumare](https://github.com/rohitg00) and the community. @ghumare64  ·  aiengineeringfromscratch.com  ·  Report / Suggest ---