From Idea to MVP: The New Builder's Playbook

The barrier to building a testable MVP has never been lower. Here is how I go from zero to shipping—and how you can too. No excuses. No gatekeeping. Just outcomes.

January 13, 202614 min readBy Jesse Alton

#product-delivery #mvp #cursor #prd #entrepreneurship

The barrier to building a testable MVP has never been lower.

I am a professional product manager and solutions architect. I have years of experience in software development, design, strategy, and sales. I am not downplaying the complexity of shipping market-ready, viable, and safe products. Building production systems that handle real user data, scale under load, and deliver consistent value is hard work.

But getting to a testable MVP? That has never been easier.

If you have product instincts, basic technical literacy, and access to AI development tools, you can go from idea to working prototype faster than ever before. I have done this repeatedly. Army of One documents what I have shipped in the last 13 months—Cadderly, peake.ai, jessealton.com, virgent.ai, alton.tech, and dozens of customer agents. All built solo with AI assistance.

This post is the playbook. The exact process I use. The tools I rely on. The workflow that works.

First: A Warning

Before you rush off to build the next unicorn, slow down.

Building an MVP is not the same as charging money for a product. Do not vibe-code your way into a lawsuit. Do not damage others because you moved fast and broke things.

If you are handling user data, you have responsibilities. GDPR compliance. Data encryption. Proper authentication. Security best practices. Terms of service. Privacy policies. These things matter.

If you skip them, you risk:

Legal liability when your database gets breached
Regulatory fines for mishandling personal information
Destroying trust with early adopters who believed in you
Burning bridges in your industry before you even get started

Build fast. Ship fast. But do not be reckless.

Use this process to validate ideas, test assumptions, and build prototypes. When you are ready to charge money or handle sensitive data, bring in experts. Hire a lawyer. Get a security audit. Do it right.

With that said, let's talk about how to actually build.

What is a PRD and Why You Need One

A Product Requirements Document (PRD) is the blueprint for what you are building. It defines:

The problem you are solving — Why does this matter?
Who it is for — What user needs are you addressing?
What it does — Core features and functionality
How it works — User flows, technical requirements, constraints
Success metrics — How will you know if it works?

Product managers live in PRDs. We use them to align teams, communicate with stakeholders, and document decisions. A good PRD prevents scope creep, keeps everyone focused on outcomes, and serves as the north star when things get chaotic.

For solo builders, a PRD does something even more valuable: it forces you to think before you code.

Too many builders jump straight into implementation without clarity on what they are actually building. They chase features instead of outcomes. They build for themselves instead of users. They waste weeks on solutions that miss the mark.

A PRD prevents that.

How to Create a PRD with AI

Here is my exact process.

Step 1: Brain dump the idea

I talk. Out loud. Into a recording device. I walk through:

What problem I am trying to solve
Who I am building this for
What outcomes I want to achieve
Why existing solutions fall short
What the MVP needs to do

No structure. No polish. Just raw thoughts.

Step 2: Transcribe and synthesize

I upload the recording to Cadderly, which transcribes it, summarizes it, and extracts key points. Cadderly is my coordination agent—it handles intent recognition, context management, and structured output generation. It turns messy voice notes into usable documents.

If you do not have Cadderly, just use ChatGPT, Claude, or Gemini. Upload an audio file or paste a transcript. Ask it to extract:

Core problem statement
Target user personas
Feature list prioritized by importance
Technical requirements and constraints
Success criteria

Step 3: Refine the PRD

AI gives you a first draft. Now make it better.

Ask follow-up questions:

What am I missing?
What assumptions am I making that could be wrong?
What is the simplest version of this that validates the core hypothesis?
What could go wrong?

Iterate until you have clarity. The PRD should be specific enough that someone else could read it and understand what you are building and why.

PRD Resources

If you want to learn more about writing great PRDs, check out:

Lenny's Newsletter on PRDs — Practical product management advice
ProductPlan's PRD Guide — Structure and templates
Gibson Biddle's product strategy resources — Strategic thinking for product leaders

Tools: Where the Magic Happens

Now comes the fun part. Building.

You have options. A lot of them. Here is what I use and recommend.

Code Editors and AI-Assisted Development

Cursor — My favorite. By far.

Cursor is a fork of VS Code with native AI integration. It understands your entire codebase, suggests edits, writes boilerplate, and helps debug. I pay for the $200/month ultra membership because I use it constantly and the value is insane.

Why Cursor over everything else?

Context awareness — It sees your whole project, not just the current file
Agent mode — Give it a task and watch it work across multiple files
Speed — Fast autocomplete, fast generation, fast iteration
Local model support — Works with Ollama for privacy-sensitive projects

Alternatives:

VS Code with GitHub Copilot — Solid, widely used, free tier available
Windsurf — Newer AI-powered editor from Codeium
Zed — Blazing fast, multiplayer-first, AI-assisted

No-Code and Low-Code Options

If you are less technical or want to prototype faster, try:

v0.dev — Vercel's AI-powered UI generator. Describe what you want, get React components.
Loveable — AI pair programmer for full-stack apps
Bolt.new — StackBlitz's AI app builder
Replit with Replit AI — Browser-based coding with AI assistance

Local Models with Ollama

I love Ollama for running local LLMs. Why local?

Privacy — Your code never leaves your machine
Cost — No API fees after initial setup
Control — Fine-tune models, adjust parameters, experiment freely

Popular models for coding:

DeepSeek-Coder — Excellent for code generation
CodeLlama — Meta's coding-focused model
Qwen2.5-Coder — Strong multilingual code support

Ollama integrates beautifully with Cursor. Best of both worlds.

The Workflow: From PRD to Working Prototype

Here is the step-by-step.

1. Set up your project structure

Create a folder on your system. Something like:

mkdir ~/code/my-project
cd ~/code/my-project

2. Open it in Cursor

Launch Cursor. Open the folder. This is your workspace.

3. Drop in your PRD

Create a file: PRD.md

Paste your entire PRD into it. This gives the AI context for every decision moving forward.

4. Enter Agent Mode

In Cursor, press Cmd+K (Mac) or Ctrl+K (Windows/Linux) to open the command palette. Select Agent Mode.

5. Tell it what you want

Example prompt:

Build a Next.js application based on the PRD.md in this project.

Use:
- TypeScript for type safety
- Tailwind CSS for styling
- LangChain for LLM orchestration
- OpenAI API for language model access
- Vercel Postgres (via Neon) for database

Start by scaffolding the project structure, then implement 
the core features listed in the PRD.

6. Watch it work

Cursor will scaffold your project, install dependencies, set up configuration files, and start building. It is not magic. It makes mistakes. But it handles 80% of the tedious setup work instantly.

7. Iterate and refine

As it builds, review the code. Ask questions:

"Why did you structure the API routes this way?"
"Can we optimize this database query?"
"Add error handling to the authentication flow"

Treat the AI like a junior developer. It is fast and capable, but you still need to guide it.

My Favorite Stack

I have built dozens of projects. This stack consistently works.

Frontend

Next.js — React framework with server-side rendering, API routes, and great developer experience
TypeScript — Catch errors before runtime. Essential for maintainability.
Tailwind CSS — Utility-first styling. Fast to prototype, easy to maintain.
shadcn/ui — Beautiful, accessible component library built on Radix UI

Backend & APIs

LangChain — Orchestration framework for LLM applications. Handles chains, agents, memory, and tool integration.
Vercel — Deploy Next.js apps instantly. Serverless functions. Edge runtime. CDN. It just works.
Hono — Lightweight, fast web framework for APIs. Great for building edge functions.

AI & Models

OpenAI — GPT-4, GPT-4o, o1. Industry-leading models. Reliable API.
Together AI — Fast inference for open-source models. Great for custom deployments.
Hugging Face — Model hub. Fine-tuning. Open-source ecosystem.
Ollama — Local model hosting. Privacy-first development.

Database

Neon — Serverless Postgres. Generous free tier. Auto-scaling. Great developer experience.
Drizzle ORM — Type-safe SQL ORM for TypeScript. Lightweight, fast, great migrations.

Observability & Debugging

LangSmith — Trace LLM calls, debug chains, monitor production agents
Sentry — Error tracking and performance monitoring

Additional Tools

Stripe — Payments. Simple integration, great docs.
Resend — Transactional email. Developer-friendly API.
Clerk — Authentication. Drop-in solution with social logins, MFA, and user management.

If you do not know these tools, just tell your AI agent. Seriously.

"I need to add authentication but I have never used Clerk. Help me set it up."

It will walk you through it. The AI knows these tools better than most developers.

Test, Iterate, Repeat

You have built your MVP. Now what?

Test it. Break it. Fix it.

Does it solve the problem you defined in the PRD?
Can a user accomplish their goal without getting confused?
What breaks under edge cases?
Where does performance degrade?

Get feedback early. Show it to potential users. Watch them use it. Do not explain anything. Just observe.

Where do they get stuck? What do they expect that is not there? What delights them?

Use that feedback to iterate. Update the PRD. Refine features. Cut what does not matter. Double down on what works.

This is the loop. Build. Test. Learn. Repeat.

Speed matters. The faster you iterate, the faster you learn. AI removes friction from implementation, so you can focus on learning what actually matters.

The Philosophy: Vibe Coding and Flow-Based Development

There is a philosophical shift happening in how we think about building software.

Computer scientist Andrej Karpathy coined the term vibe coding in early 2025 to describe this new paradigm: developers providing natural language descriptions to AI models and guiding the output rather than manually writing every line. He described it as "fully giving in to the vibes, embracing exponentials, and forgetting that the code even exists."

This is not lazy. It is a fundamental rethinking of what programming means.

Rick Rubin's "The Way of Code"

Legendary music producer Rick Rubin—the guy who produced everyone from Johnny Cash to Kanye West—has applied his creative philosophy to software development through his project The Way of Code, built in collaboration with Anthropic.

The project presents 81 concise meditations paired with interactive demos that can be edited in natural language through Claude. Rooted in Taoist principles like simplicity, receptivity, and flow, it treats code as a canvas and AI as a creative instrument.

Rubin's approach champions outcomes that emerge from dialogue with the machine rather than rigid adherence to predetermined structure. You describe intent. You iterate conversationally. You allow the system to guide the build process. The result often surprises you—in a good way.

This mirrors Rubin's music production philosophy from his book The Creative Act: A Way of Being. He emphasizes:

Removing obstacles between intention and expression
Trusting intuition over rigid process
Allowing space for emergence
Recognizing that constraints can be liberating

You can watch Rick Rubin discuss art, life, and vibe coding here.

Jake Knapp's Character Labs

On the other end of the spectrum—but equally valuable—is Jake Knapp's structured approach to rapid prototyping.

Knapp, co-author of the New York Times bestseller Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days and co-founder of Character VC, recently launched Character Labs: a four-week sprint program for early-stage startups.

Character Labs combines proven Design Sprint formats into an intensive accelerator that helps founders move toward product-market fit with velocity and discipline. It is time-boxed. Structured. Iterative. Exactly what you need when vibe coding could otherwise send you wandering down rabbit holes.

The power comes from combining these approaches:

Rubin's flow-based intuition — Let AI guide you toward unexpected solutions
Knapp's structured iteration — Time-box your experiments, validate assumptions, kill bad ideas fast

Together, they create a framework that is both creatively open and ruthlessly focused on outcomes.

Why This Matters

Most builders fall into one of two traps:

Trap 1: Analysis paralysis. They overthink architecture, debate tech stacks, and never ship.

Trap 2: Chaos shipping. They vibe-code without structure, build features nobody wants, and waste time on dead ends.

The solution is embracing both:

Use AI to remove friction and explore possibilities (Rubin's flow)
Use structured iteration to validate and refine (Knapp's sprints)
Use PRDs to maintain strategic clarity (your job as PM)

This is what I do. Every project. Every time.

I let the AI surprise me with implementation approaches I would not have considered. But I stay anchored to user problems, measurable outcomes, and time-boxed experiments.

The result? Thirteen months of constant shipping. Production systems. Measurable ROI. Real outcomes.

What a Time to Be Alive

Four years ago, this workflow did not exist.

I would have spent weeks setting up infrastructure, debugging configurations, and writing boilerplate. I would have needed a team. Or I would have been stuck waiting for someone else to build my vision.

Now? Idea to working prototype in hours. Sometimes less.

I shipped peake.ai in one hour. An AI-enhanced phone system that replaced our $300/month VoIP service. Working production system. LangChain automations. Full integration with our existing tools. One hour.

That is what is possible now.

You do not need a CS degree. You do not need a co-founder. You do not need funding.

You need:

A clear problem to solve
A PRD that defines the solution
AI tools that handle implementation
The willingness to iterate until it works

If you have been waiting for permission to build, this is it.

Stop waiting. Start shipping.