We got Ryan Carson on the pod to break down the “Ralph Wiggum” Agent and why it’s suddenly everywhere. He walks me through a simple workflow that lets an autonomous agent build a full product feature while I sleep: start with a PRD, convert it into small user stories with tight acceptance criteria, then run a looped script that ships work in clean iterations. The big idea is you’re not “vibe coding” one giant prompt—you’re giving the agent testable, bite-sized tickets and letting it execute like an engineering team. By the end, Ryan shows how this becomes repeatable (and safer) with a memory layer—agents.md for long-term notes and progress.txt for iteration-to-iteration context.
We got Ryan Carson on the pod to break down the “Ralph Wiggum” Agent and why it’s suddenly everywhere. He walks me through a simple workflow that lets an autonomous agent build a full product feature while I sleep: start with a PRD, convert it into small user stories with tight acceptance criteria, then run a looped script that ships work in clean iterations. The big idea is you’re not “vibe coding” one giant prompt—you’re giving the agent testable, bite-sized tickets and letting it execute like an engineering team. By the end, Ryan shows how this becomes repeatable (and safer) with a memory layer—agents.md for long-term notes and progress.txt for iteration-to-iteration context.
Timestamps
00:00 – Intro
02:44 – What is the Ralph Wiggum AI Agent
03:40 – Step 1: PRD Generator
06:11 – Step 2: Convert PRD to Json
09:47 – Step 3: Run Ralph
12:05 – Step 4: Ralph Picks a Task
13:14 – Step 5: Ralph Implements Task
14:49 – Tokens + Cost: What It Actually Spends
15:45 – Guardrails: Small Stories + Clear Criteria Keep It Sane
16:19 – Step 6: Ralph commits the change
16:38 – Step 7: Ralph Updates PRD json file
16:55 – Step 8: Ralph Logs to Progress txt
20:08 – Step 9: Ralph Picks another Task
20:48 – Step 10: Ralph Finishes Tasks
21:18 – Example of how Ryan uses Ralph
24:08 – How To Start Today (Ralph Repo) and Tips
Links Mentioned:
Ralph Wiggum Agent: https://startup-ideas-pod.link/Ralph-agent
AI Agent Skills: https://startup-ideas-pod.link/amp-skills
AMP: https://startup-ideas-pod.link/amp-code
Ryan’s Ralph Step-by-Step Guide: https://startup-ideas-pod.link/Ryans-Ralph-Guide
Key Points
I can’t expect “sleep-shipping” unless I translate the feature into small, testable user stories with clear acceptance criteria.
Ralph works like a Kanban loop: pull one story, implement, commit, mark pass/fail, then grab the next.
The real leverage is the reset: each iteration starts fresh with a clean context window, instead of one giant, messy thread.
agents.md becomes long-term memory across the repo; progress.txt is short-term memory across iterations.
The bottleneck isn’t “coding”—it’s the upfront spec quality: PRD clarity, atomic stories, and verifiable criteria.
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The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/
FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/
FIND RYAN ON SOCIAL:
X/Twitter: https://x.com/ryancarson
Amp: https://ampcode.com
