Build your first AI product
Step-by-step from someone who did it with zero coding knowledge. Start with a project, not courses.
Start With Something You Want to Build
Don't start with courses. Start with a project. I wanted a chatbot for my blog β that one goal drove everything else I learned.
Build With an AI Coding Agent
You don't need to know how to code β the agent writes it, and you learn to read and steer it. Make a GitHub account, learn just enough Git to commit and roll back, then build something real with a coding agent. I use Claude Code and Codex in the terminal, and Google Antigravity (the 2.0 editor and its CLI) at work. The walkthrough is the work. Tools churn fast; the judgment you build steering them is what lasts.
Resources
Codex with GPT-5.4 vs Claude Code with Opus 4.6 β Why I Now Use Both
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Why I Cancelled Claude Max After 13 Months and What Iβm Testing with Codex Next
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I Rebuilt My Site With Two AI Models: Opus for Design, Codex for Execution
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How I Rebuilt My Blog Backend in 4 Days with Claude Code
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Foundation β Your Choice, Time-Boxed
How much coursework you need depends on where you're starting. New to the vocabulary β prompting, embeddings, RAG, agents? Take a couple of foundations, time-boxed, then stop; cert-collecting is procrastination dressed up as progress. Already fluent? Skip straight to building. Here's my honest 2026 keep / time-box / skip verdict.
Learn Evals + Judgment
Once something runs, learn evaluations the practical way. Save real inputs and outputs, decide what good and bad actually look like, and let the agent help you scaffold a rubric β grounded in your real materials. What it must not do is quietly set your standard for you; you calibrate that. This judgment, not fluency with any one tool, is the durable skill.
Connect Your Agent to Your Stack (MCP)
When you're ready, learn the basics of MCP β the layer that lets your coding agent talk to the rest of your tools. Start with three that help almost any build: Context7 (so it reads current, real docs instead of guessing APIs), Playwright and Chrome DevTools (so it can drive and debug a real browser). After that, the right MCPs depend on what you're building β Supabase, Stripe, Resend, Vercel for web apps; even iOS now has an Xcode MCP. It didn't exist in 2023; it's now part of how I build.
Survive the Valley of Death
You will get stuck β I was stuck for months. Courses didn't click, a framework failed me, a tool got expensive. The only thing that worked was trying another approach and not quitting. Expect this part.
Secure It Before You Ship
AI agents write code fast β including insecure code. Before you deploy, do the unglamorous review: keep secrets and API keys out of the repo, lock down auth and row-level access, and actually read the dependencies and diffs the agent generated. My first deployment threw 200+ security warnings. You don't need a six-month certificate first; you need the discipline to check before you ship.
Ship to a Standard
Don't just ship β ship work you'd put your name on. The loop that works for me: reality-check the idea, write a brief, plan it, build it, put a real decision gate before launch, then finish a capstone you'd hand to an actual user. DIALΓGUE took six months; STRAΕ¦UM, 75 days. The first one is always the hardest. If you want to see how the pieces fit, you can explore both.
Keep Shipping: What Comes After v1
The real work starts after the AI says 'done.' Rebuilds, App Store submissions, multilingual support, performance, the boring infrastructure β here's what the second year actually looked like.