The 10-MVP Cycle: Automating the Journey from Idea to Deployment

There is a silent revolution happening in how we build software, and it has nothing to do with a new JavaScript framework. It’s about a shift in who—or what—does the heavy lifting.

Imagine this scenario: You’re waiting for a flight. You have an idea. By the time you land, you don’t just have notes in your phone; you have a fully deployed MVP with a database, authentication, and a frontend, live on a URL. You didn't write a single line of syntax. You just managed the flow.

This isn't science fiction. It's what people are starting to call "Vibe Coding," and it's fundamentally changing the economics of startups.

Most founders are still stuck in the old loop: Idea -> Hire/Code -> Debug -> Deploy. That loop is too slow. The new loop looks like this: Context -> AI Agent -> Verification.

I've spent the last few weeks refining a stack that automates the messy middle of development. It allows me to run ten potential MVPs in parallel cycles. Here is the exact setup, the workflow, and the honest truth about where it breaks down.

The "Vibe" Stack

To make this work, you need tools that talk to each other. We aren't looking for the most complex enterprise solutions; we want speed and integration. Here is the toolkit:

1. Framework: create-t3-turbo (A monorepo setup built on Turborepo that handles frontend and backend in one place. Includes Next.js, tRPC, Tailwind, and Prisma. Perfect for MVPs).
2. Brain: Claude Code (The CLI tool that acts as your lead engineer).
3. Project Management: Linear (The source of truth).
4. Tracking: GitHub Issues (Synced with Linear).
5. Infrastructure: Vercel (Hosting) + Neon (Serverless Postgres).

Both Vercel and Neon have generous free tiers, meaning your cost to experiment is effectively zero.

The Setup Phase

Before you write a line of code, you need to wire the machine. Here's the exact setup process:

1. Initialize the Project

Start by creating a new create-t3-turbo project. This gives you a production-ready monorepo structure with everything configured:

npx create-turbo@latest -e https://github.com/t3-oss/create-t3-turbo

This command scaffolds a complete stack with Next.js for the frontend, tRPC for type-safe APIs, Prisma for database management, and Tailwind CSS for styling—all in a Turborepo monorepo structure.

2. Install Claude Code

Next, install Claude Code, your AI-powered development assistant. Follow the installation guide at code.claude.com. Once installed, you can launch it from your project directory:

claude

3. Set Up GitHub Actions Integration

Inside the Claude Code CLI, install the GitHub Actions integration by running:

/install-github-app

This command sets up the GitHub Actions bot that will enable Claude to create pull requests, run CI/CD workflows, and interact with your repository. The integration allows Claude to execute code changes through automated workflows rather than just suggesting them. For detailed setup instructions, see the official GitHub Actions documentation.

4. Connect Linear to GitHub

The magic happens when you connect Linear to GitHub for bidirectional issue syncing. Here's how:

1. Go to Linear's GitHub integration page
2. Click "Add Integration" and authorize Linear to access your GitHub account
3. Select the repository you want to sync
4. Configure the sync settings:
   • Bidirectional sync: Issues created in GitHub automatically appear in Linear, and vice versa
   • Status mapping: Map Linear workflow states (Todo, In Progress, Done) to GitHub issue states
   • Priority sync: Keep priority levels synchronized between both platforms
   • Auto-linking: Automatically link branches and PRs to Linear issues using issue IDs

Once configured, when an issue is created in GitHub, it appears in Linear. When you update priority or status in Linear, the change reflects in GitHub. This creates a unified workflow where you can manage tasks from either platform.

5. Deploy Infrastructure

Finally, deploy to Vercel with a Neon database:

• Connect your GitHub repository to Vercel
• Provision a Neon serverless Postgres database (free tier available)
• Add the database connection string to your Vercel environment variables

Once deployed, you now have a continuous integration pipeline that runs from your thoughts to a live URL. Every merge to main automatically deploys to production.

The Automated Workflow

Here is where the traditional role of the Product Manager begins to fade away.

1. The Context Session

If you have an idea, don't open an IDE. Open a chat window. Talk to ChatGPT or Claude. dumping your raw thoughts. Let the AI challenge you. By the end of this session, ask it to generate a PRD (Product Requirements Document) in Markdown format.

What you get back is usually better than what most mid-level PMs produce. It’s detailed, structured, and professional.

Insight: I believe the dedicated "Product Manager" role is the first to be disrupted by AI. We are moving toward a world of "Product Designers" or "Full-Stack Creators" who handle the vision, while AI handles the specs.

2. The Handoff

Feed that Markdown PRD to your local Claude Code. This is the crucial step where AI transforms requirements into actionable work items.

Inside the Claude Code CLI, instruct it to parse the PRD and generate GitHub Issues. Claude Code uses the GitHub CLI (gh) under the hood to interact with your repository. Here's what happens:

1. Parsing: Claude analyzes the PRD and breaks it down into discrete, actionable tasks
2. Issue Creation: For each task, Claude runs commands like:

   gh issue create --title "Implement user authentication" \
     --body "Details from PRD..." \
     --label "feature" \
     --assignee "@me"
   

3. Metadata Assignment: Claude automatically assigns labels (feature, bug, enhancement), priorities, and even estimates based on the complexity described in the PRD
4. Dependency Mapping: If the GitHub Actions integration is set up, Claude can also create issue dependencies and milestones

The beauty of using gh CLI is that it's programmable—Claude can batch-create dozens of issues in seconds, each properly formatted with markdown descriptions, acceptance criteria, and technical notes.

Suddenly, your Linear board lights up. Thanks to the Linear-GitHub sync you configured earlier, you'll see 10 to 20 tickets populating automatically—properly titled, with detailed descriptions, priorities, and status. The entire backlog that would take a PM hours to create manually is generated in under a minute.

3. The Execution

Now, you act as the Engineering Manager. You tag @claude in the issues to start working on tickets. Claude writes the code in GitHub Actions, creates the Pull Request, and waits for review.

You merge.

Vercel’s CI/CD kicks in. Minutes later, the changes are live.

The beauty of this is that it can happen on your phone. You are reviewing PRs and managing the board from a taxi or a cafe. You aren't typing generic React boilerplate; you are directing a high-speed construction crew.

The "Last Mile" Problem

If you stop reading here, you might think AI has solved everything. It hasn't.

While "Vibe Coding" is powerful, we need to be honest about its limitations. AI is incredible at the "dirty work" and the "dry work." It can scaffold a database schema, build a login form, and set up API routes faster than any human.

But it struggles with the soul of the product.

Think of it like building a house. The AI is your framing crew. They can pour the concrete, put up the studs, and hang the drywall in record time. But they are terrible interior designers. They don't know that the light switch feels wrong in that corner, or that the flow of the kitchen makes people bump into each other.

In software, this is the "Last Mile." The interactions that delight users, the subtle UI animations, the edge cases in business logic—this is where you, the human builder, must step in.

If you rely 100% on the AI to finish the product, you will end up with software that works technically but feels hollow emotionally. It will lack that "craft" that separates great products from generic ones.

Practical Takeaways

If you want to try this today, here is my advice:

• Start with the PRD: Never ask the AI to "just code." The quality of the code is downstream of the quality of the PRD. Spend your energy refining the requirements text, not the code syntax.
• Trust but Verify: AI coding agents can hallucinate dependencies or write insecure logic. Read the diffs. Treat the AI like a talented but junior developer.
• Embrace the Volume: Use this speed to test more ideas. If you can build an MVP in a weekend instead of a month, your odds of finding product-market fit go up exponentially.

The barrier to building is gone. The only thing left is your taste and your persistence.