How to Vibe Code: Start With a Paid, Testable Slice

To learn how to vibe code, choose one narrow paid outcome, give the coding agent a testable first instruction, inspect the diff, and ship the smallest complete loop. Use the Claude Code documentation for the mechanics, but treat generated code as untrusted until tests and manual checks pass. ProvenStartups would start with a boring workflow whose buyer is obvious; it would refuse “build me a startup” prompts because they hide architecture, acceptance criteria, and failure states.

Contents

The shortest useful path is to select a paid vertical slice, turn it into an executable build prompt, choose a case by evidence rather than hype, and reject speed as a substitute for demand. The sections below follow that order, then answer the practical questions that block a first build.

• ·Start with a paid vertical slice
• ·Write a build prompt an agent can execute
• ·Choose a project by evidence, not hype
• ·Where the data contradicts vibe-coding advice
• ·FAQ

Start with a paid vertical slice

Start vibe coding with one input, one valuable transformation, one output, and one payment event. That is enough scope to expose whether the product works and whether anyone wants it. Do not begin with teams, referrals, dashboards, or elaborate agent systems; those features multiply uncertain code before the core transaction has earned them.

The full matching cohort contains 82 projects, including 59 solo-run projects. Only 22 publish a clean monthly figure, with a $20K/mo median and a $2K/mo to $500K/mo range. That median describes the full matching set, not just the named cases on this page.

Payout is the useful shape: a visitor discovers relevant class-action claims and can act on them. It reached $20K/mo [V] in 50 days, but the lesson is scope, not a promise that another app will repeat the result.

Define the first slice in this order:

1. 1.Name the buyer and the painful job.
2. 2.Specify the minimum input and visible output.
3. 3.Add one acceptance test for the happy path and each dangerous failure.
4. 4.Decide what event would justify another week of work.

If the slice cannot be explained without “platform,” “ecosystem,” or “eventually,” cut it again. A small, working transaction gives the agent less room to hallucinate and gives the founder something concrete to sell.

Write a build prompt an agent can execute

A useful build prompt states the repository context, the single vertical slice, technical constraints, acceptance tests, and a stop condition. It does not ask the model to make product decisions silently. Every ProvenStartups project includes a build prompt so the evidence can become an implementable starting point rather than another inspirational case study.

Take the AEO Service, which has a $2,000/mo retainer from one client [F]. The founder also reported that the client moved from invisible to recommended in eight weeks [F]. A real first instruction, derived from that project brief rather than presented as the founder’s private prompt, looks like this:

``text In this fresh TypeScript repository, build the first vertical slice of an AEO audit tool. Accept a company URL, target buyer, and purchase questions. Save the request in SQLite. Return a structured audit showing missing answer coverage and recommended source changes. Add input validation, deterministic sample data, unit tests for parsing and scoring, and a README with local setup. Do not add authentication, billing, teams, or background jobs. Stop after the tests pass and list every assumption that still needs a human decision. ``

Each line has one job:

• ·“Fresh TypeScript repository” fixes the execution context.
• ·The inputs and audit define a complete user-visible slice.
• ·SQLite and deterministic data constrain implementation choices.
• ·Tests define “done” more reliably than a polished screenshot.
• ·The exclusion list prevents speculative product expansion.
• ·The final sentence forces uncertainty into the open.

Apply the same pattern to the AI App Factory, whose revenue was not disclosed [U], or the AI Directory Site, whose $2K-$10K/mo figure is a target [C], not observed revenue. A build prompt can make either idea runnable. It cannot upgrade weak market evidence.

Choose a project by evidence, not hype

Choose the project whose smallest paid loop matches your skills, distribution, and tolerance for operational work. Revenue scale alone is a bad selector: a simple service can validate demand before software, while a large infrastructure product carries requirements a solo founder cannot copy. Compare evidence class, difficulty, and business shape together.

For a founder without distribution, a service-first loop is usually more honest. AI SEO Content Services reported $5,000+ cumulative digital-product revenue [F] plus client retainers of several thousand dollars a month [F]. The work can begin manually, reveal repeated steps, and only then become software.

ProvenStartups would refuse to use the AI Venture Studio as a benchmark. Its profitability was described as theoretically unlimited [C], which is a thesis, not disclosed revenue. Tool choice comes after this filter: across the site, 211 distinct projects mention at least one major AI coding or no-code tool, so tool popularity does not identify the right market.

Where the data contradicts vibe-coding advice

The popular claim is that faster shipping and more experiments create the advantage. ProvenStartups data contradicts it: the matching set has 82 projects and 59 solo operators, yet only 22 disclose a clean monthly figure. Code output is abundant; trustworthy proof that a buyer repeatedly pays is the scarce part.

The contradiction is sharper across the full index. ProvenStartups tracks 406 graded startup ideas, including 38 cautionary tales. Of 106 cases with a clean monthly figure, 8 are under $1K/mo, 18 are between $1K-$10K/mo, 54 are between $10K-$100K/mo, and 26 exceed $100K/mo.

Even early revenue does not erase product risk. The NoFap men’s self-improvement app made $6K/mo in its first month [V] and is still filed as a cautionary tale. “It generated code” and “it made money once” are both weaker claims than “it became a durable business.”

That is why every figure needs an evidence class. The grading method separates third-party verified [V], founder-reported [F], creator-relayed [C], and unverified [U] claims. ProvenStartups would ship after a narrow test passes, but it would not call that validation until users pay, return, and produce evidence another person can inspect.

FAQ

Vibe coding is easiest to learn as a controlled development loop, not as prompt collecting. Start with a vertical slice, make the agent expose assumptions, review its changes, and test the actual user path. The answers below cover the definition, first session, learning method, app workflow, and tool choice.

What is vibe coding?

Vibe coding means directing an AI system in natural language while it generates much of the implementation. Wikipedia’s entry on vibe coding provides the broader definition and history. For production work, the useful version still includes reading diffs, running tests, checking dependencies, and owning every failure the generated code introduces.

How do I start vibe coding?

Start with a fresh repository and a task that can finish in one visible user flow. State the stack, inputs, output, persistence choice, tests, exclusions, and stop condition. Ask the agent to list assumptions before expanding scope. Then run the software yourself and inspect the changed files; a passing transcript is not proof that the feature works.

How do I learn vibe coding without becoming dependent on the model?

Learn by predicting each change before reading the diff, then explain the result back in plain language. When the agent introduces an unfamiliar library or pattern, pause and trace the call path instead of accepting it. Reimplement small pieces manually. The goal is faster feedback with retained judgment, not maximum generated code per hour.

How do I vibe code an app instead of a demo?

Build a complete transaction through the real interface and storage layer. For a Payout-like product, that means entering location data, returning eligible claims, saving a selection, and handling invalid input before adding growth features. Payout’s $20K/mo result [V] is evidence for its case, not permission to copy its outcome assumptions.

Which AI coding tool should I use?

Use the tool that fits your repository and lets you review changes, run commands, and constrain scope. Site-wide mentions include ChatGPT in 100 projects, Claude Code in 50, Cursor in 46, and Bolt in 40, but those counts do not establish causation. Pick one, keep the prompt contract explicit, and judge it by defect rate and completed user flows.