The Startup Ideas Podcast

In this episode, I sit down with Professor Ras Mic for a beginner-friendly crash course on using Claude Code (and AI coding agents in general) without feeling overwhelmed by the terminal. We break down why your output is only as good as your inputs and how thinking in features + tests turns “vague app ideas” into real, shippable products. Was walks me through a better planning workflow using Claude Code’s Ask User Question Tool, which forces clarity on UI/UX decisions, trade-offs, and technical constraints before you build. We also talk about when not to use “Ralph” automation, why context windows matter, and how taste + audacity are the real differentiators in 2026 software.

Timestamps

00:00 – Intro

01:22 – Claude Code Best Practices

05:31 – Claude Code Plan Mode

09:30 – The Ask User Question Tool

14:52 – Don’t start with Ralph automation (get reps first)

16:36 – What are “Ralph loops” and why plans and documentation matter most

18:41 – Ras’s Ralph setup: progress tracking + tests + linting

23:48 – Tips & tricks: don’t obsess over MCP/skills/plugins

27:44 – Scroll-stopping software wins

Key Points

Your results improve fast when you treat AI agents like junior engineers: clear inputs → clean outputs.

The biggest unlock is planning in features + tests, not broad product descriptions.

Claude Code’s Ask User Question Tool forces real clarity on workflow, UI/UX, costs, and technical decisions.

If you haven’t shipped anything, don’t hide behind automation—build manually before using “Ralph.”

Context management matters: long sessions can degrade quality, so restart earlier than you think.

Numbered Section Summaries

The Real Reason People Get “AI Slop” I frame the episode around a simple idea: if you feed agents sloppy instructions, you’ll get sloppy output. Ras explains that models are now good enough that the failure mode is usually unclear inputs, not model quality.

How To Think Like A Product Builder (Features First): Ras pushes a practical mindset: don’t describe “the product,” describe the features that make the product real. If you can list the core features clearly, you can actually direct an agent to build them correctly.

The Missing Piece: Tests Between Features: We talk about the shift from “generate code” to “build something serious.” The move is writing and running tests after each feature, so you don’t stack feature two on top of a broken feature one.

Why Default Planning Mode Isn’t Enough: Ras shows the standard flow: open plan mode, ask Claude to write a PRD, and get a basic roadmap. The issue is it leaves too many assumptions—especially around UI/UX and workflow details.

The Ask User Question Tool (The Planning Upgrade): This is the big unlock. Ras demonstrates how the Ask User Question Tool interrogates you with increasingly specific questions (workflow, cost handling, database/hosting, UI style, storage, etc.) so the plan becomes dramatically more precise.

Spend Time Upfront Or Pay For It Later: We connect the dots: better planning reduces back-and-forth, reduces token burn, and prevents “I built the app but it’s not what I wanted.” The interview-style planning forces trade-offs early instead of late.

Don’t Use Ralph Until You’ve Built Without It: Ras makes a strong case for reps: if you can’t ship something end-to-end yet, automation won’t save you—it’ll just move faster in the wrong direction. Build feature-by-feature manually first, then graduate to loops.

Practical Tips: Context Discipline + Taste Wins: Ras shares a few operational habits: don’t obsess over tools like MCP/plugins, keep context usage under control, and restart sessions before quality degrades. We wrap on a bigger point: in 2026, “audacity + taste” is what makes software stand out.

The #1 tool to find startup ideas/trends - https://www.ideabrowser.com

LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/

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 MIC ON SOCIAL

X/Twitter: https://x.com/Rasmic

Youtube: https://www.youtube.com/@rasmic

Today I’m joined by Samuel Thompson, an internet capitalist who’s launched 100 companies in 10 years, and he walks me through a live, end-to-end build of an info product using AI. We break down how he goes from idea → AI-written book → mockups → Shopify product page → ad creatives in a ridiculously short amount of time. The big takeaway is that this isn’t just “info products,” it’s a repeatable launch system you can apply to e-comm, SaaS, mobile apps, and pretty much anything where customer acquisition matters. We also get into the real game: CAC vs LTV, conversion rates, and how to build what Sam calls a “rigged slot machine” you can scale.

Timestamps

00:00 – Intro

02:32 – Choosing the offer

05:36 - Writing ebook with ChatGPT (outline → chapters → upgrade quality)

07:30 – Mockups with Canva & Envato Elements

10:25 – Shopify themes that convert (Solo Drop + Elixir)

12:05 – Finding products to sell

16:28 - Building the Shopify Store

21:16 – Using ChatGPT to generate product-page copy fast

24:13 – What “good” conversion rates look like (3–5% target range)

28:51 – Bonus gifts strategy = perceived value + conversion lift

33:26 – HeyGen for AI photo/video ad assets + voice clone insight

35:37 – Canva static ads + high-performing angles

38:57 – Big picture: one person can build a “real business” with AI

Key Points

Sam’s launch loop is offer → AI asset creation → Shopify page → Meta ads → iterate on math

Start with low-friction products (ebook/info) to validate customer acquisition fast

The real framework is CAC vs AOV vs conversion rate, not “brand vibes”

3–5% conversion rate is a strong target on a direct-response product page

Use bonus gifts to increase perceived value and lift conversion

Static ads + strong angles can outperform everything when the message hits

The #1 tool to find startup ideas/trends - https://www.ideabrowser.com

LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/

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 SAMUEL ON SOCIAL:

X/Twitter: https://x.com/samuelthompson

In this episode, I sat down with Chris Koerner and we go through a set of approachable startup ideas that start low-friction but can scale if you get distribution right. We start with a potential “app ecosystem” opportunity around Facebook Marketplace, plus a product-studio framework that combines short-form video, AI, and 3D printing to validate “dumb” products via demand before you invest. We then jump to more grounded, local-first ideas—bike washing/maintenance subscriptions, bar anti-spike stickers, and even vending-machine concepts like “shiny rock” drops at trailheads. We close with a weird Pokémon-card “meme + supply control” play inspired by the Kabuto King, including Chris’s own collecting “big reveal.” From there, I dig into why PSA-style grading feels slow and expensive, and we workshop a more modern grading experience (including a livestream/packaging angle and an AI-from-photo approach).

Timestamps

00:00 – Intro

02:28 – Startup Idea 1: Facebook Marketplace App Studio

07:43 – Startup Idea 2: DTC Product Studio

17:05 – Startup Idea 3: Bike Washing/Maintenance Subscription

24:29 – Startup Idea 4: Anti-Drink-Spike Stickers

31:55 – Startup Idea 5: Shiny Rock Vending Machines

36:37 – Startup Idea 6: The Kabuto King and Card Grading

Key Points

I look for “alpha” where people are already obsessing, but the market structure is still primitive (like collectibles + grading).

I treat “distribution” as the multiplier—short-form can make “dumb” products viable if the content loop is strong.

I push for starting manually first (prove demand), then upgrading into infrastructure, subscriptions, and scale.

I pay attention to marketplaces with huge usage but weak third-party tooling—there’s often a platform-layer opportunity there.

I keep coming back to “repackaging” as a business model: same underlying thing, new wrapper, new buyer, new channel.

The #1 tool to find startup ideas/trends - https://www.ideabrowser.com

LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/

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 CHRIS ON SOCIAL:

X/Twitter: https://x.com/mhp_guy

Instagram: https://www.instagram.com/thekoerneroffice/

Youtube: https://www.youtube.com/@thekoerneroffice

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.

The #1 tool to find startup ideas/trends - https://www.ideabrowser.com

LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/

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

In this episode, I’m breaking down a guide from Ben Tossel on how you can actually build with AI agents without being technical. I walk through what he’s shipped as a “non-technical” builder, why he lives in the terminal/CLI, and the exact workflow he uses to go from idea → spec → build → iterate. We also talk about the meta-skill here: treating the model like your over-the-shoulder engineer/teacher, and using every bug as a learning checkpoint. The takeaway is simple: pick a tool, ship fast, fail forward, and build your own system as you go.

Ben’s Article: https://startup-ideas-pod.link/Ben-Tossell-Article

Timestamps

00:00 – Intro

01:04 – What Ben Has Shipped

03:21 – The Workflow: Feed Context → Spec Mode → Let The Agent Rip

07:52 – His Agent Setup

08:56 – Coding On The Go

10:07 – Things to Learn

13:33 – The New Abstraction Layer: Learning To Work With Agents

14:33 – Learning from Others

16:15 – Use The Model As Your Teacher (Ask Everything)

18:13 – Contributing to Real Products

19:13 – Why this is Different

21:31 – Asking Silly Questions

24:00 – Beyond “Vibe Coding”: A New Technical Class

24:43 – Vibe Coding is a game

27:12 – Fail Forward + Permission To Build And Throw Things Away

28:16 – Pick One Tool, Minimize Friction, Keep Shipping

Key Points

I don’t need to be a traditional engineer to ship—I can learn by watching agent output and iterating.

The terminal/CLI is the power move because it’s more capable and I can see what the agent is doing.

“Spec mode” works best when I interrogate the plan like a philosopher instead of pretending I understand everything.

agents.md becomes my portable instruction manual so every new repo starts clean and consistent.

The fastest learning path is building ahead of my capability and treating bugs as checkpoints—fail forward.

Numbered Section Summaries

The Thesis: Non-Technical Doesn’t Mean Non-Builder I open with Ben’s core claim: you can ship real software by working through a terminal with agents, even if you can’t write the code yourself—because you can read the output and learn the system over time.

Proof: What He’s Actually Shipped I run through examples Ben built—custom CLIs, a crypto tracker, “Droidmas” experiments, an AI-directed video demo system, and automations that keep projects moving even when he’s away from his desk.

The Workflow: Context → Spec Mode → Autonomy High Ben’s process is straightforward: talk to the model to load context, switch into spec mode to pressure-test the plan, link docs/repos for exploration, then let the model run while he watches and steers when needed.

http://agents.md/ The “Readme For Agents” That Follows You Everywhere I explain why agents . md matters—one predictable place to tell your agent how you want repos structured, how to commit, how to test, and what “good” looks like so each session gets smoother.

Coding On The Go: PRs, Issues, Phone, Telegram, Slack We get into the real “agent native” behavior: install the GitHub app, work via pull requests and issues, tag the agent to self-fix, and even push changes from your phone—plus using Slack as a one-person “product” with an agent in the loop.

Learning The Primitives: Bash, CLIs, VPS, Skills I cover the building blocks Ben’s learning: bash commands and repeatable terminal workflows, preferring CLIs over MCPs to save context, and using a VPS + syncing to keep projects always-on.

The Mindset Shift: The Model Is The Teacher The real unlock is treating the model like your patient expert—ask everything you don’t understand, bake “explain simply” into your agent instructions, and close knowledge gaps as they appear.

Fail Forward, Pick One, Keep Shipping I end on the playbook: build ahead of your capability, treat it like play, give yourself permission to throw things away, and stop tool-hopping—pick one system and go deep.

The #1 tool to find startup ideas/trends - https://www.ideabrowser.com

LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/

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/