394 lessons — you don't need them all
Building the full course is our job; how much you study is yours, by goal. The five tracks below are scoped for you, from 78 to 394 lessons: tap a card to see which Parts it covers, what it skips, and why skipping is fine. Once you pick a route, the course catalog trims itself to that path. You can switch tracks or return to the full catalog anytime from the top of the TOC — your progress stays.
Just want to use AI
No coding, no AI product work — you just want it to actually save you time. You'll finish knowing when it's reliable, when it's making things up, and how to ask so you get something useful.
Start hereUse AI professionally
Still no products and no coding — but you want AI as real productivity. On top of “just use it,” you add how LLMs work and a full Vibe Coding playbook: why it invents, how to set rules, so it stays steady when it works for you.
Start hereBuild AI products
PMs, designers, ops — you need to align with engineers on proposals, judge feasibility, and cost it out. After this, you can defend trade-offs in design review instead of getting waved off with “technically impossible.”
Start hereBuild it yourself
Engineers or heavy users who want to write an Agent, ship it, and run it. Every core lesson, no skips; follow all six milestones on the hands-on track. Leave the three hardcore source-code electives for when you have bandwidth.
Start hereWant it all
No trade-offs — take the three hardcore electives too: Grok Build's Rust source, DeepSeek Harness's TypeScript plugin core, and open-source models' distillation plus local deploy. Finish those three and you can take apart any Coding Agent on the market.
Start hereSolid highlight means the whole Part is in; dashed means only some lessons from that Part (count shown after); faded means this route explicitly recommends skipping. Those sections are fine — your goal just doesn't need them.
Why it's cut this way
“Just want to use AI” skips all theory and engineering, and only teaches using AI well: what it's doing, why it invents, how to ask so you get answers, what you can safely hand off. Beyond the beginner FAQ Part, three Harness-core themes stay in — context engineering, Prompt engineering, practical tips; from the collaboration-methods Part, three lessons on setting rules with AI and keeping long chats on track — useful every day even if you never write code.
“Use AI professionally” adds two blocks on top of just-using-it: the full LLM-fundamentals Part — so you know why it invents and where the edges are, and you get judgment; the full collaboration-methods Part — the four-step flow, acceptance criteria, and environment safety that keep AI steady when it works for you. Plus two programming-basics lessons on vocabulary and vectors, so knowledge-base retrieval misses start to make sense. Still skips all engineering implementation and product-design content.
“Build AI products” builds on “go pro” with the full Harness set, design patterns and evaluation, plus cost engineering and the self-test center. Aligning with engineers, judging feasibility, costing it out — that's these pieces. Skip code walkthroughs, long-running Agents, security sandboxing, and the three hardcore electives: the hands-on practicum keeps only the product-side slices (image-gen productization checklist, character consistency, what to do when the model dies); Agent Loop and MCP implementation stay for the build-it-yourself route.
“Build it yourself” takes every core lesson, including the full code walkthroughs in the hands-on practicum and the self-improvement Part. Follow the hands-on track end to end: finish the three-tier “what you can do now” tasks at each chapter end, fill all six milestones, and you'll leave with a working Agent and your own collaboration playbook. The three source-code electives aren't on this path — they read other people's implementations and don't block you from building your own.
“Want it all” adds three electives — 63 lessons — beyond the build-it-yourself route. Grok Build anatomy walks a production Coding Agent's Rust source from entry to tool calling; DeepSeek Harness unpacks the everything-is-a-plugin TypeScript base; open source, distillation & local deploy covers what open-source models actually open, how big models get small, and how to run them on your machine. These three are the hardest — and the biggest differentiator.