Build your first AI product
Step-by-step from someone who started learning to code in 2021, while working in advertising. I think fundamental training is important, so it is part of the path.
Start With Something You Want to Build
Pick something you really want to build, because that tells you what to learn. I wanted a chatbot for my blog, and that goal drove what I learned next.
Build With an AI Coding Agent
The agent writes the code, 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 Used Both (March 2026)
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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: Learn the Fundamentals
I think it is important to attend fundamental training. New to the vocabulary, such as prompting, embeddings, RAG and agents? Start with a couple of foundations. Already fluent? You can go straight to building. Here is my 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.
Resources
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. Connect MCP servers to test projects with limited permissions first, never to production keys.
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, and that is why I took the Google Cybersecurity Certificate. Learn these basics before you deploy, and check before you ship. If your app will handle real user data or payments, have someone qualified review it before launch.
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.
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