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A 3-step AI coding workflow for solo founders | Ryan Carson (5x founder)

Ryan Carson is a five-time founder who has spent the past 20 years building, scaling, and selling startups. In this episode, he shares his playbook for using AI to build products, turning “vibe coding” into a structured and scalable approach that can replace full engineering teams. *What you’ll learn:* 1. A simple three-file system that transforms chaotic AI coding into a structured, reliable process 2. How to create AI-generated PRDs and task lists that actually work 3. A step-by-step workflow using Cursor to build features systematically 4. Why slowing down to provide proper context is the secret to speeding up your AI development 5. How to use model context protocols (MCPs) to extend your AI’s capabilities beyond just coding 6. Why founders can now build entire companies with minimal engineering teams and how Ryan is doing it himself *Brought to you by:* ChatPRD—An AI copilot for PMs and their teams: https://www.chatprd.ai/howiai Notion—The best AI tools for work: https://www.notion.com/howiai *Where to find Ryan Carson:* Website: https://www.ryancarson.com/about LinkedIn: https://www.linkedin.com/in/ryancarson/ X: https://x.com/ryancarson *Where to find Claire Vo:* ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo *In this episode, we cover:* (00:00) Introduction and Ryan’s recent AI projects (03:25) Demo: Creating a PRD with Cursor (05:00) Ryan’s open source links: https://github.com/snarktank/ai-dev-tasks (09:53) Quick recap and common mistakes to avoid (11:00) Demo: Generating a task list from the PRD (15:31) The importance of context when working with LLMs (18:07) Demo: Working through tasks systematically using Cursor (18:56) Change management (20:00) How task lists save time for product managers (21:50) Demo: Using MCPs for front-end testing (24:50) Specific MCPs and what to use them for (26:45) Demo: Using Repo Prompt to gain precise control over context (31:23) Music’s role in Ryan’s development stack (32:10) Lightning round and final thoughts *Tools referenced:* • ChatGPT: https://chat.openai.com/ • Claude: https://claude.ai/ • Gemini 2.5 Pro: https://deepmind.google/models/gemini/pro/. • Repo Prompt: https://repoprompt.com/ • Task Master: https://github.com/eyaltoledano/claude-task-master/blob/main/docs/tutorial.md • Browserbase: https://browserbase.com/ • Stagehand: https://docs.stagehand.dev/integrations/mcp-server *Other references:* • PostgreSQL: https://www.postgresql.org/ • Prisma: https://www.prisma.io/ • SQL: https://www.sql.org/ • MCP: https://www.anthropic.com/news/model-context-protocol • VS Code: https://code.visualstudio.com/ _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email jordan@penname.co._

Ryan CarsonguestClaire Vohost
May 26, 202534mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Ryan Carson’s structured AI coding workflow: PRDs, tasks, context control

  1. Ryan Carson demonstrates a lightweight but structured approach to AI-assisted coding in Cursor: generate a PRD, convert it into a detailed task list, then execute tasks one subtask at a time with explicit “stop and confirm” checkpoints.
  2. The central message is that most AI coding failures come from rushing context—slowing down to provide clear requirements and a plan actually speeds development and reduces rabbit holes and reverts.
  3. He shows how Cursor “rules” (reusable prompt files) enforce consistent outputs (junior-dev-friendly PRDs, Markdown task lists with checkboxes, and task-by-task execution discipline).
  4. Beyond the core workflow, he highlights MCP servers (notably Postgres) to reduce engineering toil and Repo Prompt to precisely package repo context for larger-model deep analysis outside Cursor’s context “black box.”

IDEAS WORTH REMEMBERING

5 ideas

Don’t rush context—requirements clarity is the real accelerator.

Carson argues the most common mistake is impatience: skipping the time to explain what the AI needs to know. A short PRD + task plan reduces backtracking and makes the model’s output more reliable.

Use a PRD prompt that’s “junior developer implementable.”

Framing the PRD for a junior developer forces the model to spell out assumptions, steps, and edge cases that a “genius PhD” model might otherwise skip, improving implementation fidelity.

Convert PRDs into checklisted tasks before writing code.

A structured task list (Markdown, checkboxes, subtasks/sub-subtasks) turns vague intent into executable steps and helps the AI and human stay aligned on scope and sequencing.

Force the AI to do one subtask at a time with explicit stops.

His task-management rule makes the agent complete a single subtask, mark it done, then ask to proceed—preventing runaway changes across the codebase and making review/revert easier.

Keep planning lightweight—Markdown can beat heavy tooling.

Even though he considered automating tasks via Asana/MCP, he prefers a hand-cranked Markdown task list for visibility, editability, and lower operational overhead.

WORDS WORTH SAVING

5 quotes

I think the biggest mistake that I do, that everyone does, is they try to rush through the context.

— Ryan Carson

If we all just slow down a tiny bit and do these two steps, it speeds everything up.

— Ryan Carson

This is a PRD that’s suitable for a junior developer to understand and implement this feature.

— Ryan Carson

Nobody really knows how to do this stuff. The only way you're really gonna figure it out is by getting in here and getting your hands dirty and see what works.

— Ryan Carson

Building this new startup, I literally feel like I'm able to do all of it… But I am able, for sure, to build this company by myself.

— Ryan Carson

Cursor rules files as reusable promptsPRD generation for AI codingTask list generation from PRDsSubtask-by-subtask execution and human-in-the-loop QAContext management as the main lever for qualityMCP servers (Postgres, Browserbase/Stagehand) for tool accessRepo Prompt for explicit repo context packaging

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