The Startup Ideas Podcast

I sit down with Alex Finn to break down how he sets up Moltbot (formally Clawdbot) as a proactive AI employee he treats like a teammate named Henry. We walk through the core workflow: Henry sends a daily morning brief, researches while Alex sleeps, and ships work as pull requests for review. Alex explains the setup that makes this work; feeding the bot deep personal and business context, then setting clear expectations for proactive behavior. We cover model strategy (Opus as “brain,” Codex as “muscle”), a “Mission Control” task tracker Henry built, hardware options, and the security mindset around prompt injection and account access.

Timestamps

00:00 – Intro

02:08 – Clawdbot Overview

03:33 – The Morning Brief Workflow

05:01 - Proactive Builds: Trends → Features → Pull Requests

07:27 – The Setup: Context + Expectations For Proactivity

09:38 – The Onboarding Prompt Alex Uses

12:05 – Hunting “Unknown Unknowns” For Real Leverage

12:43 – Using the right Models for cost control

14:18 – Mission Control: A Kanban Tracker Henry Built

17:16 – The future of Human and AI workflow

22:01 – Hardware And Hosting: Cloud vs Local (Mac Mini/Studio)

25:47 – The Productivity Framework

27:10 – The Possible Evolution of Clawdbot

28:53 – Security and Privacy Concerns

33:38 – Closing Thoughts: Tinkering, Opportunity, And Next Steps

Key Points

I get the most leverage when I treat the agent like a proactive teammate with clear expectations and rich context.

Henry delivers compounding value by shipping work for review (pull requests) based on trend monitoring and conversation memory.

I separate “brain” and “muscle” by delegating heavy coding to Codex while using Opus for reasoning and direction.

I track autonomous work with a dedicated “Mission Control” board so progress stays visible over time.

I keep risk contained by controlling environment and account access, especially around email and prompt injection.

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

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Instagram: https://instagram.com/gregisenberg/

LinkedIn: https://www.linkedin.com/in/gisenberg/

FIND ALEX ON SOCIAL

Youtube: https://www.youtube.com/@AlexFinnOfficial/videos

X/Twitter: https://x.com/AlexFinnX

Creator Buddy: https://www.creatorbuddy.io/

I sit down with Furqan Rydhan, a founding team member of Applovin and cofounder Founders Inc, as he walks me through Nebula, a Slack-like workspace where every channel holds an agent that can execute real work across the tools teams already use. We watch Nebula create and edit a Google Slides deck end-to-end, including generating an image and handling failures by retrying until it lands. Furqan shows how Nebula turns one-off work into repeatable “recipes” with scheduled triggers, like adding slides daily or publishing blog posts multiple times per day. We also talk about what “business-in-a-box” looks like in the AI era; where direction, taste, and quality loops become the edge as automation gets widely available.

Timestamps:

00:00 – Intro

01:51 –Building useful agents for real work

03:34 – Nebula: a Slack-like agent workspace

05:04 – Demo: Nebula creating a Deck with Google Slides

13:25 – The “business in a box” content dream (newsletters, affiliates, ads)

14:39 – Demo: Automate Blog Posting

15:52 – What stays valuable when everyone automates

21:23 – Agent workforce and Building quality loops

25:38 – Services and agencies: delivering work with fewer humans

28:53 – Final Thoughts

Key Points

I watch Nebula run like “cloud code for everything else,” automating real work across tools and workflows.

Agents turn one-time actions into repeatable systems via triggers and schedules.

The interface mirrors Slack because work already lives in channels, threads, and context.

Quality becomes the differentiator: critics, scoring, and iteration loops upgrade outputs over time.

Service businesses and agencies scale faster when agents handle production-heavy tasks

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 FURQAN ON SOCIAL Furqan's

X: https://x.com/FurqanR

Fuqan’s personal website: https://furqan.com

Nebula: https://www.nebula.gg

In this episode, I sit down with Boris, the creator of Claude Code and one of the key builders behind Claude Cowork, to unpack what Cowork actually unlocks and how people use it in the real world. He walks through a hands-on demo where Cowork organizes files, extracts receipt data, builds a clean spreadsheet, and even drives the browser to create and share a Google Sheet. We go deep on how “agentic” work feels different when the model takes actions across your computer, your browser, and your tools. Then I shift into Boris’s viral workflow for Claude Code: parallel sessions, plan-first execution, Claude.md as a compounding team memory, and verification loops that dramatically improve output quality.

Timestamps:

00:00 – Intro

03:26 – Cowork Overview

05:51 – Demo: Folder Access + Renaming Receipts

08:23 – Demo: Turning Receipts Into A Spreadsheet

10:52 – Demo: Google Sheets + Chrome Control

15:52 – Demo: Emailing The Sheet + Parallel Tasking

22:07 – Best way to start/use with Cowork

24:22 – Where will AI and Agents Go Next

28:44 – Boris’s Claude Code Setup

41:12 – The “Claude” Pronunciation Discussion

Key Points

I use Cowork as a “doer,” not a chat: it touches files, browsers, and tools directly.

I think about productivity as parallelism: multiple tasks running while I steer outcomes.

I treat Claude.md as compounding memory: every mistake becomes a durable rule for the team.

I run plan-first workflows: once the plan is solid, execution gets dramatically cleaner.

I give Claude a way to verify output (browser/tests): verification drives quality.

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

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Instagram: https://instagram.com/gregisenberg/

LinkedIn: https://www.linkedin.com/in/gisenberg/

FIND BORIS ON SOCIAL

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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