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HOW IT ACTUALLY WORKS

From an idea to a finished product. Both halves, every step.

The first half writes the plan. The second half builds the product from it. Here is what happens inside each one, in the order it actually runs — no summary, no hand-waving. This site went through both.

  • 7steps to write the plan
  • 7stages to build the product
  • 0times an agent grades its own work

THE HARD PART

Everyone already agrees the loop is what matters.

There is a well-known way to build software with AI: three agents in a loop. One plans, one builds, one judges, and they go around again until it works. We think that is exactly right. The disagreement is not about whether you need a loop. It is about how far the loop goes.

That same write-up names three reasons agents lose the plot. They are the right three. Here is what we do about each one.

1 · WHAT GOES WRONG

It cannot carry state.

An agent forgets. Sessions end, the context window fills up, and the next one starts cold with no idea what was already done or why.

WHAT WE DO INSTEAD

The memory is a file, not a mind.

Everything the build needs to know lives in documents that outlive any single agent: the plan itself, a tracker updated as each piece lands, and the notes each agent leaves behind when it finishes. Work can stop in the middle and the next agent picks it up from the page. Nothing important is ever only inside somebody’s head.

2 · WHAT GOES WRONG

It cannot size the work.

Ask for a whole product and an agent will try to do it in one go, call something finished when it is half done, and run out of room in the middle of a feature.

WHAT WE DO INSTEAD

The work is divided before any of it starts.

The plan splits the product into numbered pieces, each with a finish line written down in advance. Then the build runs as stages, and each stage has its own exit. Nothing is called done because an agent felt done. It is done when the thing that was written down is true.

3 · WHAT GOES WRONG

It cannot judge its own output.

The agent that wrote the code also writes the test for it, and it tends to be generous with itself. Shallow tests pass. Mediocre work gets rated as good.

WHAT WE DO INSTEAD

Nobody grades their own work. Ever.

Every review is done by an agent that did not do the work. Reviewers read drafts they did not write. Testers test features they did not build. Auditors read code they never touched. And a review is not an opinion — it has to point at the exact line, and the fix has to be proved with a before-and-after.

We are not claiming a better model. We are claiming a longer loop.

None of this is secret. The instructions our agents follow are open source — the same ones we run. Read them on GitHub →

PART ONE

How the plan gets written.

This is the half you buy. It takes about 60 to 90 minutes and it runs on our servers while you watch. Pick any step to see what happens inside it.

1 of 7

You describe it. An agent asks you questions.

Write your idea in one box, in as much detail as you have. An agent reads it and comes back with one short list of questions — all at once, not one at a time. Answer the ones you care about and skip the rest. Then you get a summary of what it plans to write. Fix anything that is wrong and press go. This is the only part where you steer, so take your time here.

A vague plan makes vague software. This is where the vagueness gets removed, and it is the last chance to remove it.

Questions

  • 3drafts
  • 2review rounds
  • 60-90minutes, start to finish
  • 1file at the end

No round gets skipped because the document already looks fine. Skipping one costs more later than it saves now.

WHY IT HAS TO BE THIS LONG

A plan this detailed is what makes the next half possible.

A spec is not a prompt. A prompt is a wish. A plan is a build order: exact database tables, exact API calls, numbered steps, and a finish line for each one.

That difference is the entire reason the second half can run for hours without stopping to ask you anything. An agent that has to guess will guess — and it will guess differently in section four than it did in section two. An agent that has been told does not have to.

It is also what lets the work be split across a team of agents at once. You can only divide a job between ten workers if the job was divided up first, on paper, before anybody started.

PART TWO

How the plan becomes a working product.

This half runs on CRHQ. One agent reads the plan and runs the whole job, handing pieces of it to a team of other agents. It runs for hours, not minutes. Pick any stage to see what happens inside it.

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Set up first, then stop and ask.

Before anything gets built: a repository, one shared working branch, and a live development environment on a real server. From that point on, every piece that gets finished is already running somewhere you can go and look at it. Then the builder stops and waits for a person to approve the setup before it writes a line of the product.

There is no launch day at the end. It has been live since the first day, which is why nothing is a surprise on the last one.

Set up

  • 7stages
  • 2independent code audits
  • 3agents just to write the test plan
  • 2screen sizes, every screen

THE PART THAT IS EASY TO MISS

One agent runs the job and never does the work.

The lead agent does not write code. It reads the plan, decides what happens next, writes a full briefing for each piece of work, and hands it to a worker. Workers start with nothing — no memory of the project, no access to anything by default — so the briefing has to carry everything: what to build, which rules to follow, which files to touch, and how the result will be checked. Then the lead waits, reads what comes back, and decides whether it is good enough or goes around again.

Two things fall out of that, and they are the same two problems from the top of this page. Work can run in parallel, because every briefing is self-contained. And nothing depends on one agent staying alive, because the state lives in the documents rather than in anybody’s memory.

Lead agent

Reads the plan, writes each briefing, decides what happens next. Writes no code.

Worker

Boots from its briefing and builds one piece.

Worker

Builds a different piece at the same time, in different files.

Worker

Tests what the others shipped, and did not build any of it.

One lead agent, several workers at once, and one rule that never bends: no two workers in the same files at the same time.

WHY THIS NEEDS A HARNESS

None of this runs in one chat window on a laptop.

A loop that runs for hours needs somewhere to run. Here is what that somewhere has to do.

Agents have to be able to hand work to other agents.

One agent runs the job and gives pieces of it to a team. They work at the same time, and the lead gets woken up when each one reports back. A build this long does not finish without that.

The work has to survive a restart.

Jobs that run for hours get interrupted. Because progress is written down as it happens rather than held in memory, an interruption costs the piece that was in flight — not the build.

It has to run on a real server, not your machine.

The agents work on the same server the product runs on. They can read the logs, run the tests, and drive a browser against the live site. Every finished piece is already online.

Somebody has to notice when an agent goes quiet.

Delegated work gets watched. A stalled agent gets spotted and restarted by the system, rather than discovered by you a day later.

And afterwards, it keeps running.

Once the product is live, agents on that same server can fix things, add to it, and answer customer email on their own.

PROOF

This page is inside the product it describes.

GenerateSpecs was written as a spec first, then built from that plan by a team of agents, then taken through both review rounds and the polish round you just read about. The plan is published on this site — all 6,667 lines of it, and the instructions the agents follow are on GitHub. This page is not a description of the process. It is one of its outputs.

Six other live products were built the same way.

  • TranscriptAPI

    A YouTube transcript and search API. Around 15 million transcripts a month.

  • YouTube2Transcript

    Paste a YouTube link, get the transcript.

  • Recapio

    Tools for YouTube: summaries, transcripts, chat and research. Tens of thousands of users.

  • Zillapi

    US property data for developers and agents. Photos, taxes, schools, in one call.

  • StayingAPI

    Live prices and availability across Airbnb, Booking.com and Vrbo, in one call.

  • CRHQ

    The platform all of this runs on. Agents on real servers, working around the clock.

Start with the plan.

You can stop after the first half and take the file anywhere — most people do. The second half is there when you want it.

99 of 100 free specs left. Free ones get posted publicly.