Automate Full App Features While You Sleep

Wake Up to New Features: How the “Ralph” Loop Ships Code While You Sleep

You spend your nights sleeping. Ralph spends them building your product.

No, it’s not a dream. It’s a lean, autonomous workflow that takes your PRD (Product Requirements Doc), breaks it into bite-sized tasks, and cranks out tested code—all while you recharge. The result? You wake up to a commit-ready feature branch that runs, passes tests, and didn’t require a second of micromanagement.

Here’s how to make Ralph work for you.

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Ship While You Sleep: The Pitch

Let’s get real: your backlog is growing faster than your Git commits. But what if AI could handle the boring 80%—the CRUD, the tests, the UI nits—overnight?

With Ralph, it can.

Powered by Claude 3 Opus or another capable LLM, Ralph loops through your stories, builds features, and commits clean code. It’s like cloning yourself—but this version doesn’t need caffeine or context switches.

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The Ralph Loop: A Quick Look Under the Hood

At its core, Ralph is simple:

1. Pick the next user story.
2. Build it.
3. Run tests.
4. Commit.
5. Mark done.
6. Loop.

As long as each story fits inside a single LLM context (think: small tasks), Ralph can churn through 10+ iterations in a night.

Yes—actual new features by morning.

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Here’s What You’ll Need to Get Started

Before Ralph starts coding in the dark, you’ll need a few foundational pieces:

• A capable LLM
  Claude 3 Opus, GPT-4o, or any model that can handle long context + clean code.
• An agent runner
  Prefer AMP, Cursor, Continue—or your favorite local framework.
• A tested repo
  Ideally with unit tests, CI hooks, and some structure in place.
• A bash script
  Ralph runs headless from the terminal. No GUI, no fuss.

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Step 1: Write a Bulletproof PRD

Ralph only knows what you feed it. Garbage in… you know the rest.

Here’s how to write a PRD it can work with:

• Plain English description of the feature.
• Break it into micro user stories.
• Add acceptance criteria per story that are specific and verifiable.

Example: “Status column exists in tasks table, defaults to pending. Dropdown filters by All / Active / Completed.”

Pro tip: Dictate your PRD with Whisper, then polish it by hand.

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Step 2: Translate PRD → JSON

Ask your agent to convert the PRD into a prd.json file structured like this:

[
  {
    "title": "Add priority field",
    "size": "XS",
    "acceptance": [
      "Column `priority` (low, medium, high) exists in `tasks` table",
      "Default shown as medium",
      "UI filter works for each value"
    ],
    "passes": false
  }
]

Rules to follow:

• Stick with XS or S stories. Bigger = broken.
• Order stories based on dependency.
• Set "passes": false to start.

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Step 3: Run Ralph Like a Boss

Clone the open-source Ralph repo (ralph-autonomous-agent) and in your project root:

./ralph.sh --max-iterations=10 --prd=prd.json

Each loop, Ralph will:

1. Grab the next incomplete user story.
2. Launch a clean agent thread.
3. Feed it your repo, PRD, a progress log, and notes.
4. Write, test, and commit the change.
5. Mark the story as complete.
6. Update progress.txt and long-term agents.md.

Five minutes of setup. Hours of hands-off progress.

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Step 4: Put Memory in the Right Place

Ralph uses lightweight memory files—no agent amnesia here.

• agents.md
  Drop one into folders that need hard-earned knowledge (API oddities, DB quirks). The agent loads it before changing those files.
• progress.txt
  Gets updated every loop with thread info, commit diffs, and “what we learned.” It’s like breadcrumbs for fast iteration.

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Step 5: Teach It to Click

Back-end jobs are easy. Front-end stories? They need a dev browser.

Install a headless browser skill like Dev Browser (MIT-licensed) so the agent can:

• Load your app in the browser.
• Click, inspect, and test UI behavior.
• Confirm acceptance criteria interactively.

Let Ralph test like a junior dev without the flaky coffee.

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Step 6: Review, Merge, Repeat

By morning, your feature branch is ready for merge—as long as you gave clear requirements.

What to do next:

• Review commits.
• Run everything locally.
• File follow-ups for edge cases.
• Optionally re-run Ralph for polish.

Most overnight runs cost under $30 via Claude 3 Opus—less if your agent platform includes free daily tokens.

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Avoid These Pitfalls

You’ll learn faster avoiding the traps others already hit:

• Vague acceptance criteria? The agent stalls and spins.
• Oversized stories blow the context window. Break them down.
• Missing rollback logic means messier commits. Add: “Revert if tests fail.”
• Forget agents.md? You’ll fix the same bug twice.

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Ralph ≠ Replacement. Ralph = Amplifier.

AI won’t take your job. But if you ignore workflows like Ralph, someone else will ship faster—and with less overhead.

The real win here? You spend less time wiring up filters or fussing with forms—and more time launching real value.

Put in a few thoughtful hours upfront (tight PRD, good test coverage), and you unlock a whole new velocity.

Your move. Write a 3-line PRD, fire up the script, and let the loop spin while you sleep.

By tomorrow morning, you’ll get it.

Looking for more beginner-friendly ways to level up with AI dev tools? Start with Tixu—an easy entry point to smart workflows, hands-on learning, and that first shipped mini-project.