Build an MVP With AI: ChatGPT, Claude Code, and Codex

You can build an MVP with AI faster than you can finish the business plan you were going to use to raise the money to hire someone to build it. That is the shift. A non-technical founder with a clear idea, a free afternoon, and three tools (ChatGPT, Claude Code, and Codex) can ship a working product and put it in front of real users this week.

This is a practical guide, not a hype piece. You get a tool stack, a validation step, a real build path, and the failure modes nobody screenshots. The goal is to build an MVP with AI that is good enough to charge for, without pretending the AI did the thinking for you.

Why "vibe coding" opened the door

The on-ramp has a name. Andrej Karpathy coined "vibe coding" in February 2025: writing software by telling an AI what you want and letting it build, where you "fully give in to the vibes" and "forget that the code even exists." It went on to become Collins Dictionary's 2025 Word of the Year.

That last part is both the opportunity and the trap. Funded startups already ship this way. YC's Garry Tan has said a large share of the Winter 2025 batch had codebases that were mostly AI-generated, and Anthropic has said a large majority of its own new production code is now written by Claude. Treat those as reported figures, then note the common thread: the founders shipping this way stayed the editor, not the spectator. So can you.

The best first products to build this way

Pick a model where a small, sharp tool beats a bloated platform:

Micro-SaaS: one painful workflow, one paying niche (say, a booking tool for tattoo studios).
Internal-tool-as-a-product: the spreadsheet-plus-logic a specific industry rebuilds badly every time.
AI wrapper with a moat: a focused interface on top of an LLM where your prompt, data, or workflow is the value.
Productized service: you do the work, AI does the delivery, and the MVP is the order form plus the automation behind it.

The winners share one trait: a narrow audience that feels the pain weekly and would pay today.

The tool stack: give each AI a distinct job

The biggest beginner mistake is treating these as interchangeable chatbots. They are not. Assign roles.

ChatGPT, the thinking layer. Ideation, naming, market research, your spec, marketing copy, debugging by conversation. Decide what to build here.
Codex (OpenAI), the agentic builder. OpenAI's coding system spanning a CLI, an IDE extension, the cloud (via ChatGPT), and a GitHub bot, all sharing one account context. The CLI is open source, runs locally, and accepts image input, so you can paste a screenshot of a design and it reads it. See the Codex docs for install and access details.
Claude Code (Anthropic), the codebase agent. In Anthropic's words, an agent that "reads your codebase, edits files, and runs commands" across the terminal, IDE, desktop app, and browser. It adds subagents for parallel work and a plan mode for thinking before it touches files.
Hermes Agent (Nous Research), the self-hosted option. If you want to own the stack, this MIT-licensed agent runs across chat surfaces and the CLI on self-hosted backends. Nous advertises self-generated skills and persistent memory; verify those on your own work before you depend on them. A real third path for builders who prefer not to rely on a hosted vendor.

Rule of thumb: ChatGPT decides what; Codex and Claude Code build how.

Step 1: validate before you build

Do not skip to building. Spend one ChatGPT session pressure-testing the idea:

Ask ChatGPT to name 10 specific people who would pay for this, and why now.
Have it draft five cold outreach messages and a one-screen landing pitch.
Send the messages. Get three "yes, I'd pay" signals from real humans before you write a line of code.

If you cannot find three people who feel the pain, the problem is the idea, not the AI.

Step 2: write the spec

Agents work dramatically better against a written spec than against vibes. This single step is what separates a clean build from a thrash session. Have ChatGPT produce a short PRD: the one core user flow, the data you store, the screens, and, explicitly, what is out of scope for v1.

Save it as AGENTS.md in your project folder. Both Codex and Claude Code read this file as standing instructions, so it becomes the brief every session honors and you stop re-explaining the project from scratch.

Step 3: build the MVP with AI

Now the actual build. Work in tight loops, not one giant prompt:

Scaffold. Point Codex or Claude Code at your AGENTS.md and ask for the smallest runnable version of the core flow.
Run it. Let the agent execute commands and start the app. This is the line between an AI MVP and a folder of code that has never run, because these agents run and test where ChatGPT alone cannot.
Iterate one screen at a time. "Add login." "Now the dashboard." Small, verifiable steps.
Feed it images. Codex takes screenshots and design specs as input, so drop in a rough sketch and let it match the layout.
Add a verify gate. Before shipping a change, have the agent run your tests, or hand the diff to a separate review pass. Codex CLI runs an independent review; Claude Code has plan mode and subagents for the same job.

Treat every loop as prompt, run, read the diff, accept or correct. The reading is non-negotiable, and here is why.

Step 4: turn workflows into reusable skills

This is the most differentiated technical point in this guide, and the one that compounds your speed.

Claude Code and Codex share the same skills primitive. A skill is a folder holding a SKILL.md file with YAML frontmatter, where name and description are the required fields, that packages reusable instructions, scripts, and resources so the agent runs a workflow the same way every time. Both follow the open Agent Skills standard.

The founder payoff: write a workflow once, reuse it forever. Make a SKILL.md for "deploy to Vercel" or "run my test suite and report the failures," and you stop re-pasting the same instructions every session.

In Claude Code, invoke a skill with /skill-name; the body loads only when used, keeping context cheap. (Note for 2026: custom commands have merged into skills, so a file at .claude/commands/deploy.md and a skill at .claude/skills/deploy/SKILL.md both create /deploy, and old .claude/commands/ files still work.)
In Codex, invoke via /skills or $skillname; it loads only the name and description until you pick the skill. Codex looks in .agents/skills and its own config, not Claude Code's .claude/skills/, so do not assume one layout works for both.

Step 5: sell the first offer

Your MVP plus the three validated leads from Step 1 is a launch.

Have ChatGPT write the landing copy and a plain pricing page: one offer, one price.
Deploy. (Make this a SKILL.md so next time it is one command.)
Go back to the three people who said "I'd pay." Send the link. Ask for money, not feedback.

A paying customer in week one beats a polished product in week ten with none.

Mistakes to avoid

Treating all three tools as one. Do not make ChatGPT write code it cannot run; hand execution to Codex or Claude Code.
Skipping the spec. No AGENTS.md means drifting, inconsistent output.
Not reading the code. A 2025 CodeRabbit analysis of real-world pull requests reported that AI-co-authored code carried noticeably more issues, including more security issues, than human-written code. YC's Diana Hu put it plainly: founders need "the taste and enough training to know that an LLM is spitting bad stuff or good stuff." Read the diffs.
Ignoring reuse. Re-pasting instructions instead of writing one SKILL.md.
No verify gate. Shipping without running tests or a review pass.
Publishing with no author or disclosure. If you write about your build, Google's helpful-content guidance treats trust as the top E-E-A-T factor and expects AI assistance disclosed and first-hand experience shown. The same discipline applies to your product's marketing.

Your 30-day roadmap

Days 1–3: Pick the model. Validate with ChatGPT and real outreach. Get three "I'd pay."
Days 4–5: Write the PRD; save it as AGENTS.md.
Days 6–14: Build the core flow with Codex or Claude Code, one screen per loop, reading every diff.
Days 15–18: Add a verify gate; run a review pass over the code.
Days 19–22: Write two reusable skills, deploy and test.
Days 23–26: Build the landing page and pricing via ChatGPT; deploy.
Days 27–30: Sell to your three leads. Ship one fix from their feedback. Charge.

FAQ

Do I need to know how to code to build an MVP with AI? No, but you do need to learn to read the output and run the app. The non-technical founders who succeed treat the agent as a fast junior developer they supervise, not an oracle they trust blindly.

Should I use Codex or Claude Code? Both are agentic coders that read, edit, and run code, and both support SKILL.md skills. Most builders pair ChatGPT for thinking with one coding agent. Try Codex first if you already pay for ChatGPT, since it is included. Try Claude Code if you want deep terminal and IDE integration plus subagents. You can run both.

Is AI-generated code safe to ship? Not automatically. The CodeRabbit finding on higher security-issue rates is exactly why you add a verify gate and read diffs. Run a review pass before every deploy.

What is a SKILL.md, exactly? A folder with a SKILL.md file whose YAML frontmatter requires name and description, packaging a reusable workflow like "deploy to Vercel," so your agent runs it reliably and you never re-explain it. It works across both Claude Code and Codex through the Agent Skills open standard.

Sources and further reading