How I AIA 3-step AI coding workflow for solo founders | Ryan Carson (5x founder)
CHAPTERS
- 0:00 – 3:25
Why solo founders need a structured AI coding workflow (Ryan’s recent projects)
Claire introduces the episode theme: 2025 may be the year of vibe coding, but scalable execution needs structure. Ryan shares recent AI-enabled projects—from building a game with his kid to shipping pieces of his new startup—to set the stage for a repeatable workflow.
- 3:25 – 5:00
Cursor setup: using “Rules” files to standardize PRDs, tasks, and execution
Ryan opens Cursor (a VS Code fork) and explains why he relies on structured rule files instead of ad hoc prompting. He frames the core challenge as managing context and scope so the model can reliably implement larger changes.
- 5:00 – 9:53
Demo: Generating a PRD in Cursor (and why “junior developer” framing works)
Ryan demonstrates creating a PRD for a new feature by @including a PRD rule file and giving a clear feature request. Claire highlights a subtle but powerful instruction: make the PRD understandable for a junior developer to reduce ambiguity and over-assumptions.
- 9:53 – 11:00
Model choice, cost, and iteration habits while drafting requirements
They briefly compare preferred models and tradeoffs (Claude, Gemini, o3) and discuss the reality of paying for “max” modes. The focus is on consistency: learn one model’s strengths/weaknesses through repetition and refine prompts accordingly.
- 11:00 – 15:31
Open-source workflow assets + alternative: Task Master (and why Ryan keeps it simpler)
Ryan points viewers to his open-source rule files and mentions Task Master, a more powerful CLI-based approach. He explains he intentionally prefers a lighter-weight, more controlled workflow that stays easy to understand and maintain.
- 15:31 – 18:07
Recap: the #1 mistake—rushing context (and why slowing down speeds up)
Ryan emphasizes that most failures come from insufficient context and impatience. Creating the PRD and task list may feel slower, but it reduces rework and rabbit holes, making overall delivery faster and more reliable.
- 18:07 – 18:56
Demo: Turning the PRD into a detailed Markdown task list
Ryan uses a second rule file to convert the PRD into an executable plan: parent tasks, subtasks, and checkboxes in Markdown. The rule enforces an interactive process (draft tasks → confirm → ‘Go’) to ensure alignment before coding.
- 18:56 – 20:00
Systematic execution in Cursor: one subtask at a time with human-in-the-loop checks
Ryan demonstrates a third rule that governs how the AI should work through the task list: do one subtask, mark it complete, stop, and ask permission to continue. This structure limits scope creep and makes it easier to spot errors early.
- 20:00 – 21:50
Change management: when to commit to Git and how to decide rollback risk
They discuss practical version control habits when AI is making many changes quickly. Ryan typically commits after a parent task if the app is in a workable state, otherwise waits until the set of tasks is complete, balancing safety with momentum.
- 21:50 – 24:50
Why task lists are a PM time-saver (even without full automation)
Claire notes that even stopping after PRD → task list is valuable for PMs and engineers, because breaking work into codebase-aware steps is a common bottleneck. Ryan argues against over-engineering: a simple Markdown file is often the fastest, clearest solution.
- 24:50 – 26:45
Demo: MCPs in Cursor—headless browser testing with Browserbase/Stagehand
Ryan shows how MCP servers let Cursor control external tools via natural language. He demos Browserbase to navigate to a website and take screenshots in a cloud browser, framing it as a step toward automated front-end testing and faster bug reproduction.
- 26:45 – 31:23
Which MCPs are most useful day-to-day (Postgres as the workhorse)
Ryan explains that Postgres is his most-used MCP because it eliminates tedious context switching and manual SQL for basic checks. They connect MCPs to a broader theme: reducing engineering toil by consolidating tasks (PM, browser, DB) into one interface.
- 31:23 – 32:10
Demo: Repo Prompt for precise context control (escaping the context ‘black box’)
Ryan introduces Repo Prompt as a way to explicitly select and package repo files into a structured prompt with token counts and XML-like boundaries. He uses it when the stakes are higher and he needs certainty about exactly what the model is seeing.
- 32:10 – 34:44
Personal stack: music for flow + lightning round on how AI changes founding
Ryan jokes that EDM is a core part of his development setup, then closes with reflections on how AI rewrites what a solo founder can do. He shares his tactic for getting models back on track—polite, directive nudges to “think harder”—and where to find his work online.
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