rohitg00/ai-engineering-from-scratch

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<picture> <source media="(prefers-color-scheme: dark)" srcset="assets/readme/header-dark.svg"> <img src="assets/readme/header-light.svg" alt="AI Engineering from Scratch" width="840"> </picture> <a href="https://www.star-history.com/rohitg00/ai-engineering-from-scratch"> <picture><source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/badge?repo=rohitg00/ai-engineering-from-scratch&type=rank&theme=dark" /><source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/badge?repo=rohitg00/ai-engineering-from-scratch&type=rank" /><img alt="Star History Rank" src="https://api.star-history.com/badge?repo=rohitg00/ai-engineering-from-scratch&type=rank" /></picture> <picture><source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/badge?repo=rohitg00/ai-engineering-from-scratch&type=trending&theme=dark" /><source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/badge?repo=rohitg00/ai-engineering-from-scratch&type=trending" /><img alt="GitHub Trending Repository of the Day" src="https://api.star-history.com/badge?repo=rohitg00/ai-engineering-from-scratch&type=trending" /></picture> </a> Implement model internals, retrieval pipelines, and agent runtimes. Test them, inspect failures, and keep the code and evaluation results. **[Start learning](https://aiengineeringfromscratch.com/lesson?path=phases/00-setup-and-tooling/01-dev-environment)** · **[Choose a path](#learning-routes)** · **[Try a lab](#interactive-lab)** · **[Build a project](#project-challenges)** · **[Browse the curriculum](#contents)** Free, open source, MIT. Learn on the website, with a coding agent, or by running local code. > 542 lessons. 20 phases. Python, TypeScript, Rust, Julia. <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-1a1a1a?style=flat-square&labelColor=fafaf5" alt="MIT License"></a> <a href="ROADMAP.md"><img src="https://img.shields.io/badge/lessons-542-3553ff?style=flat-square&labelColor=fafaf5" alt="542 lessons"></a> <a href="#contents"><img src="https://img.shields.io/badge/phases-20-3553ff?style=flat-square&labelColor=fafaf5" alt="20 phases"></a> <a href="https://github.com/rohitg00/ai-engineering-from-scratch/stargazers"><img src="https://img.shields.io/github/stars/rohitg00/ai-engineering-from-scratch?style=flat-square&labelColor=fafaf5&color=3553ff" alt="GitHub stars"></a> <a href="https://aiengineeringfromscratch.com"><img src="https://img.shields.io/badge/web-aiengineeringfromscratch.com-3553ff?style=flat-square&labelColor=fafaf5" alt="Website"></a> <details> <summary>Read in your language</summary> <a href="README.md">🇬🇧 English</a> · <a href="i18n/zh/README.md">🇨🇳 简体中文</a> · <a href="i18n/zh-TW/README.md">🇹🇼 繁體中文（台灣）</a> · <a href="i18n/ja/README.md">🇯🇵 日本語</a> · <a href="i18n/ko/README.md">🇰🇷 한국어</a> · <a href="i18n/pt/README.md">🇵🇹 Português</a> · <a href="i18n/pt-BR/README.md">🇧🇷 Português (Brasil)</a> · <a href="i18n/es/README.md">🇪🇸 Español</a> · <a href="i18n/de/README.md">🇩🇪 Deutsch</a> · <a href="i18n/fr/README.md">🇫🇷 Français</a> · <a href="i18n/it/README.md">🇮🇹 Italiano</a> · <a href="i18n/nl/README.md">🇳🇱 Nederlands</a> · <a href="i18n/pl/README.md">🇵🇱 Polski</a> · <a href="i18n/cs/README.md">🇨🇿 Čeština</a> · <a href="i18n/ro/README.md">🇷🇴 Română</a> · <a href="i18n/hu/README.md">🇭🇺 Magyar</a> · <a href="i18n/el/README.md">🇬🇷 Ελληνικά</a> · <a href="i18n/sv/README.md">🇸🇪 Svenska</a> · <a href="i18n/da/README.md">🇩🇰 Dansk</a> · <a href="i18n/no/README.md">🇳🇴 Norsk</a> · <a href="i18n/fi/README.md">🇫🇮 Suomi</a> · <a href="i18n/ru/README.md">🇷🇺 Русский</a> · <a href="i18n/uk/README.md">🇺🇦 Українська</a> · <a href="i18n/tr/README.md">🇹🇷 Türkçe</a> · <a href="i18n/he/README.md">🇮🇱 עברית</a> · <a href="i18n/ar/README.md">🇸🇦 العربية</a> · <a href="i18n/fa/README.md">🇮🇷 فارسی</a> · <a href="i18n/hi/README.md">🇮🇳 हिन्दी</a> · <a href="i18n/bn/README.md">🇧🇩 বাংলা</a> · <a href="i18n/ur/README.md">🇵🇰 اردو</a> · <a href="i18n/th/README.md">🇹🇭 ไทย</a> · <a href="i18n/vi/README.md">🇻🇳 Tiếng Việt</a> · <a href="i18n/id/README.md">🇮🇩 Bahasa Indonesia</a> · <a href="i18n/tl/README.md">🇵🇭 Tagalog</a> </details> ### Sponsors <a href="https://serpapi.com/ai-engineering-from-scratch"><picture><source media="(min-width: 768px)" srcset="assets/sponsors/serpapi-banner-compact.png" width="48%"><img src="assets/sponsors/serpapi-banner-compact.png" alt="SerpApi. Web Search API for your AI apps. Available in Markdown and JSON for any integration." width="440"></picture></a> Your support keeps every lesson free and open source. <a href="#supporters">See all supporters</a> · <a href="SPONSORS.md">Become a sponsor</a> <a id="see-what-you-will-build-and-keep"></a> <a id="learning-routes"></a> ## Learning paths | Route | Starting lesson | |---|---| | Model foundations | [Setup and tooling](https://aiengineeringfromscratch.com/lesson?path=phases/00-setup-and-tooling/01-dev-environment) | | LLM systems | [Prompt engineering](https://aiengineeringfromscratch.com/lesson?path=phases/11-llm-engineering/01-prompt-engineering) | | Agents and delivery | [The agent loop](https://aiengineeringfromscratch.com/lesson?path=phases/14-agent-engineering/01-the-agent-loop) | [Compare career paths](https://aiengineeringfromscratch.com/learning-paths.html) · [Prerequisites and study time](#study-guide) <a id="interactive-lab"></a> ### Gradient descent Twenty starting points follow gradient descent on a quadratic loss. The graph shows their positions and mean loss after each update. <a href="https://aiengineeringfromscratch.com/lesson?path=phases/01-math-foundations/08-optimization#loss-landscape-visualization"> <picture> <source media="(max-width: 600px) and (prefers-color-scheme: dark) and (prefers-reduced-motion: reduce)" srcset="assets/readme/101-gradient-mobile-dark.png"> <source media="(max-width: 600px) and (prefers-reduced-motion: reduce)" srcset="assets/readme/101-gradient-mobile-light.png"> <source media="(prefers-color-scheme: dark) and (prefers-reduced-motion: reduce)" srcset="assets/readme/101-gradient-dark.png"> <source media="(prefers-reduced-motion: reduce)" srcset="assets/readme/101-gradient-light.png"> <source media="(max-width: 600px) and (prefers-color-scheme: dark)" srcset="assets/readme/101-gradient-mobile-dark.gif"> <source media="(max-width: 600px)" srcset="assets/readme/101-gradient-mobile-light.gif"> <source media="(prefers-color-scheme: dark)" srcset="assets/readme/101-gradient-dark.gif"> <img src="assets/readme/101-gradient-light.gif" alt="Gradient descent moves scattered starting points toward the loss minimum. The mean loss decreases with each update." width="840"> </picture> </a> [Adjust the learning rate in the lesson](https://aiengineeringfromscratch.com/lesson?path=phases/01-math-foundations/08-optimization#loss-landscape-visualization) · [Compare GD, momentum, and Adam in code](phases/01-math-foundations/08-optimization/code/optimizers.py) <a id="project-challenges"></a> ### Projects Three projects with staged starters, reference implementations, and local graders. Run commands from the repository root after [setup](#local-setup). Starters fail until you implement the stages. <details> <summary>01 · Retrieval Evaluation Lab · Python · Ranking metrics and regression checks</summary> A candidate improves mean NDCG while one query ranks its most relevant evidence lower. Build a query-by-query comparison that reports the regression and can fail a release check. Use Python 3.10+. Review [RAG](phases/11-llm-engineering/06-rag/docs/en.md) and [model evaluation](phases/02-ml-fundamentals/09-model-evaluation/docs/en.md). Implement ranking validation, precision and recall, rank-sensitive metrics, then system comparison. ```bash python3 scripts/project_test.py retrieval-evaluation-lab \ --init learning-artifacts/retrieval-evaluation-lab python3 scripts/project_test.py retrieval-evaluation-lab \ --stage 1 --path learning-artifacts/retrieval-evaluation-lab --strict python3 scripts/project_test.py retrieval-evaluation-lab \ --all --path learning-artifacts/retrieval-evaluation-lab --strict ``` **Keep:** a reproducible comparison with per-query deltas and the judgments used to score them. Metrics describe those judgments; they do not establish answer correctness. [Start the project](https://aiengineeringfromscratch.com/project.html?id=retrieval-evaluation-lab) · [Inspect the reference](projects/retrieval-evaluation-lab/solution/) · [Run on your own inputs](projects/retrieval-evaluation-lab/README.md#run-with-your-own-inputs) </details> <details> <summary>02 · Agent Trace Debugger · TypeScript · Trace parsing and timing</summary> A supplied trace still takes 100 ms, but total token usage rises by 200 and one span starts failing. Separate overlapping child work from parent execution time, then produce a report that exposes the change. Use Node.js 22.18+ and Python 3 for the grader. Implement JSONL parsing, parent validation, interval arithmetic, then an inspectable timeline. ```bash python3 scripts/project_test.py agent-trace-debugger \ --init learning-artifacts/agent-trace-debugger python3 scripts/project_test.py agent-trace-debugger \ --stage 1 --path learning-artifacts/agent-trace-debugger --strict python3 scripts/project_test.py agent-trace-debugger \ --all --path learning-artifacts/agent-trace-debugger --strict ``` **Keep:** the input trace, an HTML timeline, and a JSON regression report. Preserve exclusive per-span token counts so parent and child usage are not counted twice. [Start the project](https://aiengineeringfromscratch.com/project.html?id=agent-trace-debugger) · [Inspect the reference](projects/agent-trace-debugger/solution/) · [Explore timing interactively](https://aiengineeringfromscratch.com/project.html?id=agent-trace-debugger&stage=03-timing) </details> <details> <summary>03 · Tool Call Firewall · Rust · Role checks and approval receipts</summary> A write changes after review, or an approval is replayed. Validate the call envelope, check the caller's role and path, then consume an approval bound to the exact request and content. Use Rust and Python 3.10+. Review [tool schema design](phases/13-tools-and-protocols/05-tool-schema-design/docs/en.md) and [security boundaries](phases/17-infrastructure-and-production/25-security-secrets-audit/docs/en.md). The caller application supplies identity; the model proposes an operation. ```bash python3 scripts/project_test.py tool-call-firewall \ --init learning-artifacts/tool-call-firewall python3 scripts/project_test.py tool-call-firewall \ --stage 1 --path learning-artifacts/tool-call-firewall --strict python3 scripts/project_test.py tool-call-firewall \ --all --path learning-artifacts/tool-call-firewall --strict ``` **Keep:** an audit receipt showing the requested operation and the policy decision. Approvals are single-use within one invocation; this project does not provide persistent authorization or an operating-system sandbox. [Start the project](https://aiengineeringfromscratch.com/project.html?id=tool-call-firewall) · [Inspect the reference](projects/tool-call-firewall/solution/) · [Explore approval boundaries](https://aiengineeringfromscratch.com/project.html?id=tool-call-firewall&stage=03-consume-a-request-bound-approval-once) </details> [Browse all projects](https://aiengineeringfromscratch.com/projects.html) · [Career practice guide](learning-paths/CAREER-PRACTICE.md) ## Choose how to learn ### On the website Open any completed lesson on [aiengineeringfromscratch.com](https://aiengineeringfromscratch.com) or expand a phase under [Contents](#contents). No setup, no cloning. ### With an AI tutor If Node.js, `npx`, and a skill-capable coding agent are already installed, your coding agent can become your tutor. A repository clone is not needed to install or read the tutor. Runnable focused-path labs need `python3`. Agent Skills host labs also need a selected host and a writable user or project skill scope. ```bash npx skills add rohitg00/ai-engineering-from-scratch ``` Choose the host and scope when the installer asks. Use `start-learning` in Codex, `/start-learning` in Claude Code, or ask your host to use the skill by name. <details> <summary>Tutor setup and host commands</summary> Check the local requirements first: ```bash node --version npx --version python3 --version ``` `skills` writes to the host and scope selected during installation, such as `.claude/skills/`, `.cursor/skills/`, `.codex/skills/`, or another supported skills folder. Verify that the selected host discovers that exact destination. Invocation syntax belongs to the host, not to the portable `SKILL.md` format: | Host | Start the course | Start Model Context Protocol (MCP) | Start Agent Skills | Run a phase quiz | |---|---|---|---|---| | Codex | `start-learning`, or choose it from `/skills` | `learn-mcp`, or choose it from `/skills` | `learn-agent-skills`, or choose it from `/skills` | `check-understanding 13`, or choose it from `/skills` | | Claude Code | `/start-learning` | `/learn-mcp` | `/learn-agent-skills` | `/check-understanding 13` | | Other compatible hosts | `Use start-learning to begin the course.` | `Use learn-mcp to start the Model Context Protocol (MCP) path.` | `Use learn-agent-skills to start the Agent Skills Engineering path.` | `Use check-understanding to quiz me on Phase 13.` | 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, the `learn` skill teaches one lesson per session: concept, math, code, quiz. It streams lessons straight from this repo, and the `course-guide` skill jumps you to the exact lesson that covers anything you are stuck on. In Codex, invoke these skills with `learn` and `course-guide`; in Claude Code, use `/learn` and `/course-guide`; in other compatible hosts, ask to use the skill by name. Only want Model Context Protocol (MCP)? Use the MCP invocation for your host. It creates `MCP-LEARNING.md` and follows one 17-lesson route through stateless requests, transports, bidirectional work, security, reliability, registry governance, and conformance evidence. The exact order and checkpoints live in the [Model Context Protocol (MCP) manifest](learning-paths/model-context-protocol.json). Only want Agent Skills? Use the Agent Skills invocation for your host. It creates `AGENT-SKILLS-LEARNING.md` and follows one coherent five-lesson route: contract, discovery, invocation, sandbox boundaries, then release evals and real-host portability. Start on the web with the [Agent Skills path](https://aiengineeringfromscratch.com/lesson?path=phases/13-tools-and-protocols/22-skills-and-agent-sdks&learningPath=agent-skills). The installer lists the hosts it can configure and asks where to install. If you do not have Node.js, `npx`, `python3`, a supported host, or a writable scope yet, use the website or read `docs/en.md` manually. That path teaches the concepts, but real-host discovery, invocation, script, and uninstall evidence remains pending until the preflight is available. Read the lessons at [aiengineeringfromscratch.com](https://aiengineeringfromscratch.com). ### The learning skills | 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. | | [`learn-mcp`](skills/learn-mcp/SKILL.md) | Focused Model Context Protocol (MCP) tutor. Creates `MCP-LEARNING.md`, follows the 17-lesson manifest, and records wire, security, reliability, and conformance evidence. | | [`learn-agent-skills`](skills/learn-agent-skills/SKILL.md) | Focused Agent Skills tutor. Creates `AGENT-SKILLS-LEARNING.md`, teaches lessons 22, 24, 25, 26, and 27, and records real-host evidence. | | [`learn-reverse-engineering`](skills/learn-reverse-engineering/SKILL.md) | Focused Reverse Engineering tutor. Creates `REVERSE-ENGINEERING-LEARNING.md`, teaches Phase 13 lessons 32 to 45 on programs you build yourself, and records lab evidence. | | [`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. | | [`mcpa-certification`](skills/mcpa-certification/SKILL.md) | MCPA tutor. Follows the 34-lesson `mcpa-f` route on the 2026-07-28 protocol; teaches each lesson; runs labs and the wire checker; administers the diagnostic and three 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 <phase>`](skills/check-understanding/SKILL.md) | Per-phase quiz, eight questions, with feedback and specific lessons to review. Use the Codex, Claude Code, or natural-language form in the invocation table above. | </details> <a id="local-setup"></a> ### Run local code ```bash git clone https://github.com/rohitg00/ai-engineering-from-scratch.git cd ai-engineering-from-scratch python3 phases/00-setup-and-tooling/01-dev-environment/code/verify.py --route beginner python3 phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py ``` The preflight separates requirements needed now from tools needed later. Every required failure includes the detected reason and a corrective command. The `vectors.py` command is a dependency-free lesson and ends by showing that a matrix times a vector is the operation inside a neural network layer. Save that terminal output as your first evidence. <details> <summary>Use every lesson the same way</summary> ### Use every lesson the same way 1. **Read** `docs/en.md` and explain the core idea in your own words. 2. **Type and build** the important code instead of treating the code block as decoration. 3. **Run** the lesson command from the repository root, the directory containing `README.md` and `phases/`. 4. **Keep evidence**: the command, working directory, exit code, meaningful output, and the artifact you changed or produced. 5. **Continue** only when you can explain the output and make one small change without guessing. Commands in lesson pages are paths from the repository root unless the lesson explicitly says to change directories. If a lesson offers several languages, run the implementation for the language you are learning. </details> <a id="study-guide"></a> ## Choose a learning path You do not need to scan 542 lessons before beginning. Pick one goal. Each link opens the same curriculum on GitHub or the website, and both versions use the same lesson code. | Your goal | Learn on GitHub | Learn on the website | |---|---|---| | I am new and want the complete foundation | [Phase 0: Setup and Tooling](phases/00-setup-and-tooling/) | [Dev Environment](https://aiengineeringfromscratch.com/lesson?path=phases/00-setup-and-tooling/01-dev-environment) | | I know Python and want math plus ML foundations | [Phase 1: Math Foundations](phases/01-math-foundations/) | [Linear Algebra Intuition](https://aiengineeringfromscratch.com/lesson?path=phases/01-math-foundations/01-linear-algebra-intuition) | | I want to build production LLM applications | [Phase 11: LLM Engineering](phases/11-llm-engineering/) | [Prompt Engineering](https://aiengineeringfromscratch.com/lesson?path=phases/11-llm-engineering/01-prompt-engineering) | | I want to build agents | [Phase 14: Agent Engineering](phases/14-agent-engineering/) | [The Agent Loop](https://aiengineeringfromscratch.com/lesson?path=phases/14-agent-engineering/01-the-agent-loop) | | I want to use coding agents on real repositories | [Agent-Assisted Engineering path](learning-paths/using-coding-agents.json) | [Agent-Assisted Engineering](https://aiengineeringfromscratch.com/lesson?path=phases/14-agent-engineering/31-agent-workbench-why-models-fail&learningPath=using-coding-agents) | | I want to shape the right build before implementation | [Product Judgment and Delivery path](learning-paths/shaping-the-build.json) | [Product Judgment and Delivery](https://aiengineeringfromscratch.com/lesson?path=phases/14-agent-engineering/47-outcomes-before-output&learningPath=shaping-the-build) | Not sure where you fit? Use the [`start-learning` placement tutor](skills/start-learning/SKILL.md) or the [website prerequisites guide](https://aiengineeringfromscratch.com/prereqs.html). Compare four core domains and six career routes in the [AI Engineering Learning Paths](https://aiengineeringfromscratch.com/learning-paths.html). <details> <summary>Focused MCP, Agent Skills, and Reverse Engineering paths</summary> | Your goal | Learn on GitHub | Learn on the website | |---|---|---| | I want to build with Model Context Protocol (MCP) | [Model Context Protocol (MCP) route](phases/13-tools-and-protocols/README.md#model-context-protocol-mcp-path) | [Model Context Protocol (MCP) path](https://aiengineeringfromscratch.com/lesson?path=phases/13-tools-and-protocols/06-mcp-fundamentals&learningPath=model-context-protocol) | | I want to write and ship Agent Skills | [Focused Agent Skills route](phases/13-tools-and-protocols/README.md#agent-skills-fast-path) | [Agent Skills path](https://aiengineeringfromscratch.com/lesson?path=phases/13-tools-and-protocols/22-skills-and-agent-sdks&learningPath=agent-skills) | | I want to read compiled programs | [Reverse Engineering route](phases/13-tools-and-protocols/README.md#reverse-engineering-path) | [Reverse Engineering path](https://aiengineeringfromscratch.com/lesson?path=phases/13-tools-and-protocols/32-program-anatomy-elf-from-scratch&learningPath=reverse-engineering) | </details> <details> <summary>Prerequisites and study time</summary> ### Prerequisites - You can write code (any language; Python helps). - You want to understand how AI **actually works**, not just call APIs. ## 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 | | Only want to build production MCP systems | [Model Context Protocol (MCP) path](learning-paths/model-context-protocol.json) | ~23 hours 15 min | | Only want to build production Agent Skills | [Agent Skills Engineering path](learning-paths/agent-skills.json) | ~9.5 hours | | Only want to learn reverse engineering | [Reverse Engineering path](learning-paths/reverse-engineering.json) | ~32.5 hours | </details> ## 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 ``` <a id="contents"></a> ## Contents Twenty phases. Click any phase to expand its lesson list. <a id="phase-0"></a> ### Phase 0: Setup & Tooling `13 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 | | 13 | [Python for AI Engineering](phases/00-setup-and-tooling/13-python-for-ai-engineering/) | Build | Python | <details id="phase-1"> <summary>Phase 1 — Math Foundations  <code>22 lessons</code>  The intuition behind every AI algorithm, through code.</summary> | # | 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 | </details> <details id="phase-2"> <summary>Phase 2 — ML Fundamentals  <code>21 lessons</code>  Classical ML — still the backbone of most production AI.</summary> | # | 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 | | 19 | [ML System Design — From Problem to Deployed Model](phases/02-ml-fundamentals/19-ml-system-design/) | Build | Python | | 20 | [Monitoring Models in Production — Data Drift and Concept Drift](phases/02-ml-fundamentals/20-model-monitoring-and-drift/) | Build | Python | | 21 | [Feature Stores and Training-Serving Skew](phases/02-ml-fundamentals/21-feature-stores-and-training-serving-skew/) | Build | Python | </details> <details id="phase-3"> <summary>Phase 3 — Deep Learning Core  <code>13 lessons</code>  Neural networks from first principles. No frameworks until you build one.</summary> | # | 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 | </details> <details id="phase-4"> <summary>Phase 4 — Computer Vision  <code>28 lessons</code>  From pixels to understanding — image, video, 3D, VLMs, and world models.</summary> | # | 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 | </details> <details id="phase-5"> <summary>Phase 5 — NLP: Foundations to Advanced  <code>29 lessons</code>  Language is the interface to intelligence.</summary> | # | 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 | </details> <details id="phase-6"> <summary>Phase 6 — Speech & Audio  <code>17 lessons</code>  Hear, understand, speak.</summary> | # | 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 | </details> <details id="phase-7"> <summary>Phase 7 — Transformers Deep Dive  <code>16 lessons</code>  The architecture that changed everything.</summary> | # | 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 | </details> <details id="phase-8"> <summary>Phase 8 — Generative AI  <code>15 lessons</code>  Create images, video, audio, 3D, and more.</summary> | # | 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 | </details> <details id="phase-9"> <summary>Phase 9 — Reinforcement Learning  <code>12 lessons</code>  The foundation of RLHF and game-playing AI.</summary> | # | 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 | </details> <details id="phase-10"> <summary>Phase 10 — LLMs from Scratch  <code>24 lessons</code>  Build, train, and understand large language models.</summary> | # | 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 | </details> <details id="phase-11"> <summary>Phase 11 — LLM Engineering  <code>17 lessons</code>  Put LLMs to work in production.</summary> | # | 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 | </details> <details id="phase-12"> <summary>Phase 12 — Multimodal AI  <code>25 lessons</code>  See, hear, read, and reason across modalities — from ViT patches to computer-use agents.</summary> | # | 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 | </details> <details id="phase-13"> <summary>Phase 13 — Tools & Protocols  <code>45 lessons</code>  The interfaces between AI and the real world.</summary> | # | 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: Stateless Requests and JSON-RPC](phases/13-tools-and-protocols/06-mcp-fundamentals/) | Learn | Python | | 07 | [Building an MCP Server: Stateless Python and TypeScript](phases/13-tools-and-protocols/07-building-an-mcp-server/) | Build | Python, TypeScript | | 08 | [Building an MCP Client: Discovery, Routing, and Dual-Era Fallback](phases/13-tools-and-protocols/08-building-an-mcp-client/) | Build | Python | | 09 | [MCP Transports: stdio and Stateless Streamable HTTP](phases/13-tools-and-protocols/09-mcp-transports/) | Learn | Python | | 10 | [MCP Resources and Prompts: Addressable Context for Stateless Servers](phases/13-tools-and-protocols/10-mcp-resources-and-prompts/) | Build | Python | | 11 | [MCP Model Input: Sampling Migration and Stateless MRTR](phases/13-tools-and-protocols/11-mcp-sampling/) | Build | Python | | 12 | [Explicit Scope and Stateless Elicitation](phases/13-tools-and-protocols/12-mcp-roots-and-elicitation/) | Build | Python | | 13 | [MCP Tasks Extension: Durable Work on a Stateless Core](phases/13-tools-and-protocols/13-mcp-async-tasks/) | Build | Python | | 14 | [MCP Apps on the Stateless Protocol](phases/13-tools-and-protocols/14-mcp-apps/) | Build | Python | | 15 | [MCP Security: Poisoned Metadata, Routing, and MRTR State](phases/13-tools-and-protocols/15-mcp-security-tool-poisoning/) | Learn | Python | | 16 | [MCP Authorization: CIMD, Issuer Binding, PKCE, and Step-Up](phases/13-tools-and-protocols/16-mcp-security-oauth-2-1/) | Build | Python | | 17 | [Stateless MCP Gateways and Registry Admission](phases/13-tools-and-protocols/17-mcp-gateways-and-registries/) | Learn | Python | | 18 | [MCP Auth in Production: Issuer-Bound Enrollment and Tokens](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 | [Agent Skills: Portable Contract and Runtime Boundary](phases/13-tools-and-protocols/22-skills-and-agent-sdks/) | Build | Python | | 23 | [Capstone: Stateless Tool Ecosystem](phases/13-tools-and-protocols/23-capstone-tool-ecosystem/) | Build | Python | | 24 | [Skill Discovery and Progressive Disclosure](phases/13-tools-and-protocols/24-skill-discovery-and-progressive-disclosure/) | Build | Python | | 25 | [Skill Invocation and Routing](phases/13-tools-and-protocols/25-skill-invocation-and-routing/) | Build | Python | | 26 | [Skill Permissions, Sandboxes, and Trust](phases/13-tools-and-protocols/26-skill-permissions-sandboxes-and-trust/) | Build | Python | | 27 | [Skill Evals, Packaging, and Portability](phases/13-tools-and-protocols/27-skill-evals-packaging-and-portability/) | Build | Python | | 28 | [MCP Tool Contracts and Content](phases/13-tools-and-protocols/28-mcp-tool-contracts-and-content/) | Build | Python | | 29 | [MCP Reliability, Cancellation, and Flow Control](phases/13-tools-and-protocols/29-mcp-reliability-cancellation-and-flow-control/) | Build | Python | | 30 | [MCP Registry Supply Chain: Admission, Drift, and Rollback](phases/13-tools-and-protocols/30-mcp-registry-supply-chain-and-drift/) | Build | Python | | 31 | [MCP Conformance Engineering: Versioning, Evidence, and Operations](phases/13-tools-and-protocols/31-mcp-conformance-versioning-and-operations/) | Build | Python | | 32 | [Program Anatomy: An ELF Reader From Scratch](phases/13-tools-and-protocols/32-program-anatomy-elf-from-scratch/) | Build | Python | | 33 | [Disassembly and Control Flow: From Bytes to a Graph You Can Check](phases/13-tools-and-protocols/33-disassembly-and-control-flow/) | Build | Python | | 34 | [Lifting to Pseudocode: IR, SSA, and Structuring From Scratch](phases/13-tools-and-protocols/34-lifting-to-pseudocode/) | Build | Python | | 35 | [Evidence, Confidence, and Unknowns in Binary Analysis](phases/13-tools-and-protocols/35-evidence-confidence-and-unknowns/) | Build | Python | | 36 | [Rebuild and Prove Equal: Differential Tests, Byte Comparison, and the Evidence Loop](phases/13-tools-and-protocols/36-rebuild-and-prove-equal/) | Build | Python | | 37 | [The Reverse Engineering Landscape: History, Tool Families, and How a Decompiler Is Built](phases/13-tools-and-protocols/37-the-reverse-engineering-landscape/) | Build | Python | | 38 | [Calling Conventions and Stack Frames: System V, Windows x64, and AArch64](phases/13-tools-and-protocols/38-calling-conventions-and-stack-frames/) | Build | Python | | 39 | [Compiler Idioms: What Optimization Does to Your Code and How to Read It Back](phases/13-tools-and-protocols/39-compiler-idioms-and-optimization/) | Build | Python | | 40 | [Linking, Loading, and Stripped Binaries: PLT, GOT, Imports, Fixups, and Function Identification](phases/13-tools-and-protocols/40-linking-loading-and-stripped-binaries/) | Build | Python | | 41 | [Recovering Data and Types: Structs, Arrays, C++ Objects, and DWARF](phases/13-tools-and-protocols/41-recovering-data-and-types/) | Build | Python | | 42 | [Dynamic Analysis of Your Own Program: Debuggers, Traces, and Confirming Static Claims](phases/13-tools-and-protocols/42-dynamic-analysis-of-your-own-program/) | Build | Python | | 43 | [Beyond Native Code: Python, JVM, .NET, and WebAssembly Bytecode](phases/13-tools-and-protocols/43-bytecode-and-managed-formats/) | Build | Python | | 44 | [Binary Diffing and Symbolic Execution: Matching Functions and Solving for Inputs](phases/13-tools-and-protocols/44-binary-diffing-and-symbolic-execution/) | Build | Python | | 45 | [AI-Assisted Reverse Engineering: LLM Decompilers, Analysis Agents, and Ground Truth](phases/13-tools-and-protocols/45-ai-assisted-reverse-engineering/) | Build | Python | Lessons 06-18 and 28-31 form the focused [Model Context Protocol (MCP) path](learning-paths/model-context-protocol.json). Its manifest order is 06, 07, 08, 09, 10, 11, 12, 13, 14, 15, 16, 18, 17, 28, 29, 30, 31. Start it with the host-specific `learn-mcp` invocation above. Lesson 23 is its only optional capstone and also requires Lessons 19 and 20. Lessons 22 and 24-27 form the focused [Agent Skills learning path](learning-paths/agent-skills.json), from package contract through real-host release gates. Start it with the host-specific `learn-agent-skills` invocation shown above; do not follow numeric next navigation from 22 to 23. </details> <details id="phase-14"> <summary>Phase 14 — Agent Engineering  <code>54 lessons</code>  Build agents from first principles, use coding agents reliably, and shape the work before implementation.</summary> | # | 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 | | 43 | [Frame the Task Before the Agent Writes Code](phases/14-agent-engineering/43-frame-the-task-before-code/) | Build | Python | | 44 | [Build an Evidence-Backed Execution Plan](phases/14-agent-engineering/44-plan-from-evidence/) | Build | Python | | 45 | [Delegate Agent Work with Isolation and Merge Contracts](phases/14-agent-engineering/45-delegate-with-isolation/) | Build | Python | | 46 | [Turn Every Agent Correction into a System Improvement](phases/14-agent-engineering/46-turn-feedback-into-system/) | Build | Python | | 47 | [Define the Outcome Before You Choose the Output](phases/14-agent-engineering/47-outcomes-before-output/) | Build | Python | | 48 | [Discover the Workflow People Actually Perform](phases/14-agent-engineering/48-discover-the-real-workflow/) | Build | Python | | 49 | [Map Assumptions and Resolve the Riskiest One First](phases/14-agent-engineering/49-map-assumptions-and-risk/) | Build | Python | | 50 | [Choose the Smallest Slice That Can Change the Decision](phases/14-agent-engineering/50-choose-the-smallest-testable-slice/) | Build | Python | | 51 | [Write Specifications That Preserve Judgment](phases/14-agent-engineering/51-write-specifications-that-preserve-judgment/) | Build | Python | | 52 | [Design Success Metrics Before the Result Exists](phases/14-agent-engineering/52-design-success-metrics/) | Build | Python | | 53 | [Choose Prototype, Pilot, or Production Deliberately](phases/14-agent-engineering/53-prototype-pilot-or-production/) | Build | Python | | 54 | [Build a Feedback Ratchet with Ownership and Retirement](phases/14-agent-engineering/54-build-the-feedback-ratchet/) | Build | Python | Each Phase 14 workbench lesson (31-42) ships a `mission.md` briefing the agent before it opens the full lesson docs. Lessons 31-46 form the [Agent-Assisted Engineering path](learning-paths/using-coding-agents.json). Its manifest order combines the workbench foundation with task framing, planning, delegation, and durable feedback. Lessons 47-54 form the [Product Judgment and Delivery path](learning-paths/shaping-the-build.json), from outcome framing through evidence, risk, scope, measurement, staged release, and feedback ownership. </details> <details id="phase-15"> <summary>Phase 15 — Autonomous Systems  <code>22 lessons</code>  Long-horizon agents, self-improvement, and the 2026 safety stack.</summary> | # | 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 | </details> <details id="phase-16"> <summary>Phase 16 — Multi-Agent & Swarms  <code>25 lessons</code>  Coordination, emergence, and collective intelligence.</summary> | # | 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 | </details> <details id="phase-17"> <summary>Phase 17 — Infrastructure & Production  <code>29 lessons</code>  Ship AI to the real world.</summary> | # | 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 | | 29 | [Model Demo Interfaces — From a Function to a Shareable App](phases/17-infrastructure-and-production/29-model-demo-interfaces/) | Build | Python | </details> <details id="phase-18"> <summary>Phase 18 — Ethics, Safety & Alignment  <code>30 lessons</code>  Build AI that helps humanity. Not optional.</summary> | # | 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 | </details> <details id="phase-19"> <summary>Phase 19 — Capstone Projects  <code>85 lessons</code>  17 end-to-end products + 9 deep-build tracks. 20-40 hours per project; 4-12 lessons per track.</summary> | # | 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 | [Stateless 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 | </details> ## Books and certifications <details> <summary>Read the core curriculum as a book</summary> 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). </details> <details> <summary>Prepare for Claude certifications</summary> 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` in Codex, `/claude-certification` in Claude Code, or ask another host to use `claude-certification`. It chooses a track, creates a persistent route in `CLAUDE-CERTIFICATION.md`, teaches one step at a time, runs the real labs, and gives 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. </details> <details> <summary>Prepare for MCP Associate (MCPA)</summary> The [MCPA Certification Curriculum](certifications/mcpa/README.md) is a free, open-source preparation program for the Model Context Protocol Associate exam from the Agentic AI Foundation, delivered through Linux Foundation Training. Its 34 lessons teach the stateless 2026-07-28 protocol across the five exam domains: per-request `_meta` and `server/discover` in place of the old handshake, multi round-trip requests, subscriptions, caching, the tasks and MCP Apps extensions, OAuth authorization, and the registry and SDK tiers. Every lesson ships a runnable standard-library lab whose transcript is checked for the current wire shape, and the track adds a diagnostic, a capstone, and three full-length original practice exams whose question mix follows the published blueprint weights. Use the [AI-native GitHub onboarding guide](certifications/mcpa/GETTING_STARTED.md) with Claude Code, Codex, ChatGPT, Cursor, or another agent. Run `mcpa-certification` in Codex, `/mcpa-certification` in Claude Code, or ask another host to use `mcpa-certification`. It creates a persistent route in `MCPA-CERTIFICATION.md`, teaches one step at a time, runs the real labs, and gives artifact-based feedback. The same curriculum is available on the [MCPA track page](https://aiengineeringfromscratch.com/certification?id=mcpa-f). This curriculum is independent study material based on public exam objectives. It is not affiliated with the Agentic AI Foundation or the Linux Foundation, does not reproduce live exam questions, and cannot guarantee a passing score. </details> ## The toolkit Every lesson produces a reusable artifact. Install one in your agent or use the scripts below from the repository root. <details> <summary>Lesson structure and reusable artifacts</summary> ## The shape of a lesson Each lesson lives in its own folder, with the same structure across the entire curriculum: ```text phases/<NN>-<phase-name>/<NN>-<lesson-name>/ ├── 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"] ``` ## Every lesson ships something Other curricula end with *"congratulations, you learned X."* Each lesson here ends with a **reusable tool** you can install or paste into your daily workflow. <table> <tr> <th align="left" width="25%"><img src="site/assets/figures/001-a-prompts.svg" width="120" height="120" alt="FIG_001.A prompts"/> FIG_001 · A PROMPTS</th> <th align="left" width="25%"><img src="site/assets/figures/001-b-skills.svg" width="120" height="120" alt="FIG_001.B skills"/> FIG_001 · B SKILLS</th> <th align="left" width="25%"><img src="site/assets/figures/001-c-agents.svg" width="120" height="120" alt="FIG_001.C agents"/> FIG_001 · C AGENTS</th> <th align="left" width="25%"><img src="site/assets/figures/001-d-mcp-servers.svg" width="120" height="120" alt="FIG_001.D MCP servers"/> FIG_001 · D MCP SERVERS</th> </tr> <tr> <td valign="top">Paste into any AI assistant for expert-level help on a narrow task.</td> <td valign="top">Drop into Claude, Cursor, Codex, OpenClaw, Hermes, or any agent that reads <code>SKILL.md</code>.</td> <td valign="top">Deploy as autonomous workers — you wrote the loop yourself in Phase 14.</td> <td valign="top">Plug into any MCP-compatible client. Built end-to-end in Phase 13.</td> </tr> </table> </details> <details> <summary>Install lesson artifacts</summary> **The lesson artifacts.** The repo ships 414 skills and 100 prompts under `phases/**/outputs/`; install them via `scripts/install_skills.py`. Requires cloning the repo. Supports tag filters, dry-runs, and per-agent layouts: ```bash python3 scripts/install_skills.py <target> # every skill, default --layout skills (nested) python3 scripts/install_skills.py <target> --layout skills # same as above, explicit python3 scripts/install_skills.py <target> --type all # skills + prompts + agents python3 scripts/install_skills.py <target> --phase 14 # one phase only python3 scripts/install_skills.py <target> --tag rag # filter by tag python3 scripts/install_skills.py <target> --layout flat # flat files python3 scripts/install_skills.py <target> --dry-run # preview without writing python3 scripts/install_skills.py <target> --force # overwrite existing files ``` `<target>` is the skills directory for your agent (examples: `~/.claude/skills/`, `~/.cursor/skills/`, `~/.config/openclaw/skills/`, `.skills/`, or any path your agent reads). By default the script refuses to overwrite an existing destination and exits with code 1 after listing every colliding path. Use `--dry-run` to preview collisions or `--force` to overwrite. Every non-dry-run run writes a `manifest.json` in the target with the full inventory grouped by type and phase. Pick the layout your agent reads: | `--layout` | Path written | |---|---| | `skills` | `<target>/<name>/SKILL.md` (nested convention, supported by Claude / Cursor / Codex / OpenClaw / Hermes) | | `by-phase` | `<target>/phase-NN/<name>.md` | | `flat` | `<target>/<name>.md` | </details> <details> <summary>Drop the agent workbench into your own repo</summary> 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/`. </details> <details> <summary>Browse the entire course as JSON</summary> `scripts/build_catalog.py` walks every phase, every lesson, every artifact on disk and writes `catalog.json` at the repo root. One file, every course truth. ```bash python3 scripts/build_catalog.py # writes <repo>/catalog.json python3 scripts/build_catalog.py --stdout # to stdout, do not touch repo python3 scripts/build_catalog.py --out path/to/file.json ``` The catalog is filesystem-derived, not README-derived, so counts always match what is actually on disk. Use it for site builds, downstream tooling, or to verify the README counts have not drifted. Schema is documented at the top of the script. The curriculum workflow builds `catalog.json` as an ephemeral, gitignored artifact. Do not commit it. The same workflow runs `audit_lessons.py` as a blocking check. </details> <details> <summary>Smoke-check every lesson's Python code</summary> `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 <deps>`. The script is opt-in and not wired into CI. Stdlib only, Python 3.10+. Set `LINK_CHECK_SKIP=domain1,domain2` to override the default skip-list (`twitter.com`, `x.com`, `linkedin.com`, `instagram.com`, `medium.com` — domains that aggressively block automated HEAD/GET). </details> <details> <summary>Foundational papers and protocols</summary> - *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) </details> ## 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. <a id="supporters"></a> ## Sponsor the work  114,584 readers  ·  181,995 page views in the last 30 days  ·  as of 2026-08-29  Free, MIT-licensed, 542 lessons. Thank you to the sponsors and backers who make the work possible. [See all sponsors and backers](BACKERS.md). Want to support the work? See [sponsorship options](SPONSORS.md), including [hardware sponsorships](SPONSORS.md#hardware-lab-partner), or [sponsor on GitHub](https://github.com/sponsors/rohitg00). 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. <a href="https://x.com/ghumare64">@ghumare64</a>  ·  <a href="https://aiengineeringfromscratch.com">aiengineeringfromscratch.com</a>  ·  <a href="https://github.com/rohitg00/ai-engineering-from-scratch/issues/new/choose">Report / Suggest</a>

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このリポジトリの skills

その他の高等教育教員ソフトウェア品質保証アナリスト・テスターその他コンピュータ職ソフトウェア開発者

6 件の職業カテゴリ · 68% 分類済み

skill

職業分類

説明

更新

course-guide

未分類

Topic router for the AI Engineering from Scratch curriculum. Give it a topic, a question, or a bug you are fighting, and it points at the exact lessons that teach it, plus the right next command. Trigger phrases: "where do I learn", "which lesson covers",…

原文の言語: 英語

2026-10-14

learn-reverse-engineering

未分類

Focused interactive tutor for the Reverse Engineering path in AI Engineering from Scratch. Start or resume this route when a learner wants to read a compiled program: file formats, disassembly, control flow, lifting, evidence, proof, calling conventions,…

原文の言語: 英語

2026-10-14

learn

未分類

Interactive lesson tutor for the AI Engineering from Scratch curriculum. Reads LEARNING.md, fetches the next lesson, teaches it section by section in the terminal, quizzes at the end, and records progress. Works cloned or entirely over…

原文の言語: 英語

2026-10-14

start-learning

未分類

One-time onboarding for the AI Engineering from Scratch curriculum (523 lessons, 20 phases). Interviews the learner, runs the placement quiz, and writes LEARNING.md, a persistent study plan the learn skill drives. Trigger phrases: "start learning", "set up…

原文の言語: 英語

2026-10-14

blueprint-figure

未分類

Draws static SVG figures for lessons, READMEs and manuals in the AI Engineering from Scratch look: paper panel, blueprint accent, nine figure kinds, fixed hue meanings, eight arrow styles, and checks for legibility and markup. Trigger phrases: "draw a…

原文の言語: 英語

2026-10-13

course-style

未分類

The shared visual and writing reference for AI Engineering from Scratch pages, figures, and manuals. Use it for light and dark colour tokens, font stacks, FIG_ label formats, and card layout. Use it for the plain technical English rules and the accessibility…

原文の言語: 英語

2026-10-13

isometric-explainer

未分類

Builds an interactive isometric machine page. Each station of one invented machine stands for one step of a process. A pure, tested state model drives the motion, the readouts and the copy. The page has controls and a verification loop. Trigger phrases:…

原文の言語: 英語

2026-10-13

motion-figure

未分類

Builds figures that move or respond to the reader, in three tiers: SMIL animations and slider widgets inside lessons, interactive mechanism figures that show the named parts of a real system with a computed verdict, and ordered dither bands in the course…

原文の言語: 英語

2026-10-13

write-manual

未分類

Writes a long technical manual that explains one subject at one pinned version, from its purpose to each request and response. Every listing comes from an offline capture, every claim traces to a ranked source, and an audit must report TOTAL 0. Use it for…

原文の言語: 英語

2026-10-13

build-project

その他の高等教育教員

Hands-on project tutor for the AI Engineering from Scratch Projects section. Guides a learner through one stage of a real project per session: read the stage lesson, predict, write the code, run the stage grader, reflect, and record progress in…

原文の言語: 英語

2026-09-29

check-tests

ソフトウェア品質保証アナリスト・テスター

Run the local test evidence check

原文の言語: 英語

2026-09-29

release-publish

ソフトウェア開発者

Publish an approved Orchard release

原文の言語: 英語

2026-09-29

release-review

ソフトウェア品質保証アナリスト・テスター

Review Orchard deployment manifests

原文の言語: 英語

2026-09-29

orchard-review

ソフトウェア品質保証アナリスト・テスター

Review a release proposal

原文の言語: 判定不能

2026-09-29

orchard-review

ソフトウェア品質保証アナリスト・テスター

Review a release proposal

原文の言語: 判定不能

2026-09-29

orchard-release

ソフトウェア品質保証アナリスト・テスター

Check deployment evidence and recovery conditions.

原文の言語: 判定不能

2026-09-29

mcpa-certification

その他の高等教育教員

AI-native tutor and onboarding workflow for the MCPA (Model Context Protocol Associate) certification in AI Engineering from Scratch. Use when a learner wants to prepare for the MCPA, resume their certification path, learn the next lesson interactively, run…

原文の言語: 英語

2026-09-25

check-understanding

その他の高等教育教員

Phase quiz for AI Engineering from Scratch. Trigger with "quiz me", "test phase", "check my understanding", "do I know phase 3", or `/check-understanding <phase>`.

原文の言語: 英語

2026-08-30

find-your-level

その他の高等教育教員

Interactive quiz that maps your AI/ML knowledge to a starting point in the 523-lesson, 20-phase AI Engineering from Scratch curriculum. Trigger phrases: "where should I start", "find my level", "what do I know", "which phase", "assess my knowledge",…

原文の言語: 英語

2026-08-30

learn-agent-skills

その他の高等教育教員

Focused interactive tutor for the Agent Skills Engineering path in AI Engineering from Scratch. Start or resume this route when a learner wants to create, discover, invoke, secure, evaluate, package, or port Agent Skills. Teaches one lesson per invocation and…

原文の言語: 英語

2026-08-30

learn-mcp

その他の高等教育教員

Focused interactive tutor for the Model Context Protocol (MCP) path in AI Engineering from Scratch. Start or resume this route when a learner wants to build, secure, debug, verify, or operate MCP clients, servers, transports, gateways, registries, or…

原文の言語: 英語

2026-08-30

skill-contract-reviewer

その他コンピュータ職

Validate an Agent Skill package and choose the right instruction, capability, or lifecycle primitive before implementation.

原文の言語: 英語

2026-08-23

skill-catalog-builder

その他コンピュータ職

Build a bounded Agent Skill catalog across explicit discovery scopes and report collisions before loading instruction bodies.

原文の言語: 英語

2026-08-23

skill-invocation-router

その他コンピュータ職

Design and test explicit human, implicit model or agent, programmatic application, bounded skill-composition, and harness activation policies for an Agent Skill catalog.

原文の言語: 英語

2026-08-23

skill-safety-reviewer

情報セキュリティアナリスト

Review a skill-requested filesystem, command, network, secret, or destructive action against an explicit sandbox policy without executing it.

原文の言語: 英語

2026-08-23

skill-release-gate

その他コンピュータ職

Evaluate an Agent Skill bundle for structural integrity, trigger quality, artifact improvement, script correctness, safety, installed-tree integrity, and target-host portability before release.

原文の言語: 英語

2026-08-23

migration-review

データベースアーキテクト

Review database migration files when a change adds or modifies paths under migrations/. Use it before merge to collect forward, rollback, locking, and data-safety evidence.

原文の言語: 英語

2026-08-09

claude-certification

その他の高等教育教員

AI-native tutor and onboarding workflow for the four independent Claude certification tracks in AI Engineering from Scratch. Use when a learner wants to choose a Claude certification, prepare for CCAO-F, CCDV-F, CCAR-F, or CCAR-P, resume a certification path,…

原文の言語: 英語

2026-08-09

収集済み skill 28 件中 28 件を表示しています。

AI アシスタントでインストール

このプロンプトをコピーして、使用中の AI アシスタントに送ってください。

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/rohitg00/ai-engineering-from-scratch.