The Startup Ideas Podcast

I sit down with Remy Gaskell to break down how anyone can build AI agents to run entire departments of their business. Remy walks through the core concepts: agent loops, context files, memory, MCP tool connections, and skills. We put everything together by building a fully functional executive assistant live on screen. This is a beginner-friendly crash course that covers Claude Code, Codex, Cowork, Antigravity, Manus, and OpenClaw, showing that once you understand how to "drive," you can jump into any agent platform. By the end, listeners know exactly how to set up markdown-based context files, connect their everyday tools, and create reusable skills that compound over weeks and months.

Timestamps

00:00 – Intro

01:35 – Agents vs Chat

03:22 – The Agent Loop

05:46 – How Agents work

06:39 – Demoing Agents (Claude Code, Codex, Antigravity)

08:52 – Security and Agent Permissions

10:43 – Comparing Results Across Three Platforms

13:57 – Startup Idea: Cold Email Website Offer

14:50 – Folder Structure and Department-Based Agents

15:52 – Onboarding an Agent Like a Real Employee

17:05 – Voice-to-Text With Monologue and WhisperFlow

18:04 – Chat Memory vs. Agent Memory

19:34 – Building the agents md

22:20 – Context Engineering Over Prompt Engineering

24:29 – How Memory Compounds and Reduces Errors

30:27 – How Big Can memory md Get?

31:43 – Connecting Tools via MCP (Model Context Protocol)

34:49 – Working in Claude Code for High-Value Tasks

37:09 – Why the Real Value Is in Stacking, Not Summarizing

40:04 – What Are Skills? (SOPs for AI)

43:08 – Creating Skills

48:36 – Real-World Example: Ads Analyst Skill: 4-Hour Process in Minutes

50:37 – Chaining Skills together

52:01 – Real-World Example: Automated Car Search

53:34 – OpenClaw and Migrating Agents to More Autonomous Platforms

55:19 – Which Platform Should Beginners Start With?

56:28 – Global vs. Project-Level Skills, Context, and MCPs

Key Points

Agent platforms (Claude Code, Codex, Cowork, Antigravity, Manus, OpenClaw) are all running the same observe-think-act loop under the hood — learning one means you can use any of them.

The shift from chat to agents requires moving from prompt engineering to context engineering: load the agent with rich context so simple prompts produce excellent results.

A memory md file creates a self-improving loop where the agent learns preferences across sessions and makes fewer errors over time.

MCP (Model Context Protocol), built by Anthropic, acts as a universal translator between your agent and every tool it needs — Gmail, Calendar, Stripe, Notion, and more.

Skills are reusable SOPs packaged as markdown files; once you explain a process once, you can invoke it repeatedly, and they compound as you add three to five per week.

Scheduled tasks turn skills into automated workflows — morning briefs, car searches, ad library analyses — that run on a cron without any manual trigger.

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 REMY ON SOCIAL

X:https://x.com/remy_gaskell

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

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

I break down Andrej Karpathy's new open-source project, Autoresearch: what it is, how it works, and why some of the smartest people in tech are losing their minds over it. I walk through 10 concrete business ideas you can build on top of Autoresearch loops, from niche agent-in-a-box products to always-on A/B testing agencies. I also cover Karpathy's companion launch, Agent Hub, share community reactions, and show you step by step how to get started using Claude Code and a Colab GPU.

I'm hosting a free workshop so you can build your business in the age of AI.

Sign up here: https://startup-ideas-pod.link/build-with-ai-2026

Links Mentioned:

Autoresearch Github: https://startup-ideas-pod.link/autoresearch

Timestamps

00:00 – Intro

00:45 – How Autoresearch Actually Works

02:40 – Visual Walkthrough of the Autoresearch Loop

03:37 – Mental Model: Your Research Bot That Runs While You Sleep

05:26 – Idea 1: Niche Agent-in-a-Box Products

06:48 – Idea 2: A/B Testing for Marketing (Landing Pages & Ads)

08:45 – Idea 3: Research as a Service

09:43 – Idea 4: Power Tool Inside Your Own SaaS

10:49 – Idea 5: Agency That Runs 100× More Tests

12:05 – Idea 6: Auto Quant for Trading Ideas

13:44 – Idea 7: Always-On Lead Qualification & Follow-Up

14:21 – Idea 8: Finance Ops Autopilot for Businesses

15:09 – Idea 9: Internal Productivity Lab for Your Org

15:53 – Idea 10: Done-for-You Research & Due Diligence Shop

16:41 – Non business use cases

18:27 – Karpathy's Agent Hub Announcement

19:50 – How to Get Started with Autoresearch

22:21 – Final Thoughts

Key Points

Autoresearch is an open-source AI agent that sets a goal, runs experiments in a loop on a GPU, keeps the winners, and discards the rest — all while you sleep.

You need an NVIDIA GPU to run it (tested on H100), but you can rent one cheaply through Lambda Labs, Vast AI, RunPod, Google Cloud, or Google Colab.

The fastest way to get started is to use Claude Code to walk you through installation, then run it on Google Colab with a T4 GPU runtime.

Ten business ideas built on Autoresearch span niches like SaaS optimization, A/B testing agencies, trading backtests, CRM lead scoring, and done-for-you due diligence.

Karpathy also launched Agent Hub — essentially a GitHub designed for agent swarms to collaborate on the same codebase.

The project already has 25,000+ GitHub stars and is growing fast; early movers who tinker now build an unfair advantage.

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/

I sit down with Oliver Henry, a full-time employee who is generating hundreds of dollars in monthly recurring revenue from mobile apps he barely touches, thanks to an AI marketing agent he built on OpenClaw called Larry. We walk through how Larry autonomously creates TikTok slideshow content, reads analytics, iterates on hooks and CTAs, and feeds performance data back into the content loop. Oliver also shares how he packaged the entire system as a free, downloadable skill on Larry Brain so anyone can replicate it. By the end of the episode, you will understand the full “Larry Loop”—from content creation to conversion optimization and why skills are poised to reshape how we think about SaaS altogether.

I'm hosting a free workshop so you can build your business in the age of AI.

Sign up here: https://startup-ideas-pod.link/build-with-ai-2026

Links Mentioned:

Larry Brain: https://startup-ideas-pod.link/Larry-brain

QMD Skill: https://startup-ideas-pod.link/qmd-skill

Timestamps

00:00 – Intro

01:25 – Background on Marketing IOS app with OpenClaw

06:43 – Larry’s first posts and iterating

03:55 – Posting Strategy and First viral hit: 137K views

12:01 – Communicating with Larry via WhatsApp

12:53 – Mission control vs. single-agent workflow

14:36 – The CTA problem: views without conversions

17:07 – The Larry Loop explained: analytics → content → metrics → iterate

18:15 – Boomers, engagement bait, and the algorithm boost

20:33 – The importance of iteration

23:36 – How Larry brainstorms and validates new hooks

27:57 – The power of OpenClaw

30:04 – The vision for Larry

31:49 – Model choices: Claude vs. OpenAI and over-optimization

34:38 – OpenClaw vs. cloud alternatives (Manus, Cowork)

37:39 – Getting started: Larry Brain onboarding and 80+ skills

40:13 – Ernesto Lopez: $70K MRR using the Larry Loop

41:27 – Doing all of this with a full-time job

42:28 – QMD Skill for cutting token usage and closing thoughts

Key Points

An AI agent (Larry) built on OpenClaw autonomously creates TikTok slideshows, reads analytics, and iterates on content—driving hundreds of dollars in MRR with almost zero manual effort.

The “Larry Loop” is a full-funnel feedback cycle: TikTok analytics feed into content creation, and app metrics feed back into the top of the funnel so the agent continuously improves.

Posting TikTok content as a draft (rather than directly via API) lets you add trending sounds and avoids the algorithm penalty for bot-posted content.

Hooks drive views; CTAs drive conversions. Diagnosing which is underperforming is the key to scaling.

OpenClaw skills are locally owned, fully editable, and free from hosting or subscription costs—Oliver argues they will change how we think about SaaS.

Picking a model (Claude or OpenAI) matters far less than learning how to work with it; 98% of users will see little difference between incremental model upgrades.

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 OLIVER ON SOCIAL

X: https://x.com/oliverhenry

Larry Brain: https://www.larrybrain.com

I walk through a complete 30-step playbook for building a modern SaaS company using AI agents, media, and sub-niche positioning. The core argument is that SaaS is evolving rather than dying, and the builders who win are the ones who combine a focused workflow product with a media flywheel and agent-powered execution. Drawing on my experience advising TikTok, Reddit, and building three venture-backed companies, I lay out a step-by-step framework any solo builder or small team can follow from niche selection through to becoming the default execution layer in their market.

I'm hosting a free workshop so you can build your business in the age of AI.

Sign up here: https://startup-ideas-pod.link/build-with-ai-2026

Timestamps

00:00 – Intro

01:18 – Step 1: Start with a sub-niche inside a big market

02:21 – Step 2-5: Map Workflow end to end

06:37 – Step 6-7: Create scroll-stopping content

10:15 – Steps 8–9: Double down on organic and run paid ads on winners

11:11 – Step 10: Capture emails from day one

11:47 – Steps 11–13: Manually perform the workflow and document every step

13:40 – Steps 14–16: Turn mechanical tasks into agent workflows and connect to real tools

14:47 – Step 17: Add orchestration, retries, and verifications

16:32 – Steps 18–19: Store user preferences and launch with high-touch onboarding

18:20 – Steps 20–21: Publish measurable proof and move to per-task pricing

21:21 – Steps 22–23: Outcome pricing and compounding value

22:07 – Steps 24–27: Expand workflows, build switching costs, create case studies

23:25 – Steps 28–30: Hire from the niche, reinvest profits, become the default layer

24:08 – Closing thoughts

Key Points

Start in a specific sub-niche, not a broad market — that is where sustainable cash flow lives, not VC competition.

The future of SaaS starts as a service business: manually performing the workflow is how I learn what to automate.

Media is a core business function, not an afterthought — content creation runs in parallel with product development from day one.

Mechanical tasks are AI's strongest suit; separating judgment tasks from mechanical tasks is the key architectural decision.

Per-task and outcome-based pricing is replacing per-seat models, and indie builders have a structural advantage in making that shift.

Orchestration — coordinating agents, validating outputs, and resolving issues — is the new interface layer and the highest-value position to own.

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/

I sit down with Cody Schneider, growth engineer and co-founder of Graph, for a live, hands-on crash course in GTM (go-to-market) engineering powered by Claude Code. Cody walks through how he runs multiple AI agents simultaneously to handle everything from bulk Facebook ad creation and LinkedIn outreach to cold email campaigns and live data analysis — tasks that used to require a team of dozens. By the end of the episode, you'll have a full understanding of how to set up your own agent workflow, the specific tools involved, and why domain expertise paired with AI is the real competitive advantage right now.

Cody’s GTM Toolkit:

AI/Agent Tools: Claude Code, Perplexity API, OpenAI Codex

Marketing & Outreach: Instantly AI (cold email), Phantom Buster (LinkedIn scraping/automation), Apollo API (data enrichment), Million Verifier (email verification), Raphonic (podcast host scraping):

Advertising: Facebook Ads API, Facebook Ads Library (competitor research), Nano Banana Pro (AI image generation), Kai AI (bulk image generation), HeyGen API (UGC/video generation)

Infrastructure & Deployment: Railway.com (servers, on-the-fly databases/Postgres), Vercel (deployment)

Data & Analytics: Graphed / Graphed MCP (data warehouse, live data feeds), Google Analytics 4

CRM & Communication: Salesforce (mentioned as comparison), Intercom, SendGrid API, Slack, Cal.com API

Productivity & Design: Notion, Super Whisper (voice transcription), Claude Code front-end design skill, HTML to Canvas (for converting React components to PNGs)

Timestamps

00:00 – Intro

02:02 – What Is GTM Engineering?

05:12 – Setting Up Your Agent Workspace & Environment File

07:54 – Live Demo: LinkedIn Auto-Responder

09:56 – Live Demo: Bulk Facebook Ad Generator

12:31 – Live Demo: Cold Email Campaign Automation (Raphonic + Instantly)

14:47 – Live Demo: Creating Notion Documents via Claude Code

16:46 – Live Demo: Bulk Ad Creative Generator

26:05 – Live Demo: LinkedIn Engagement Scraper to Cold Email Pipeline

28:16 – Context Switching Across Tasks

29:19 – Live Demo: Bulk Ad Generator

31:41 – Live Demo: Data Analysis: Turning Off Low-Performing Ads

35:28 – Summary of GTM Engineering Workflow

37:48 – Deploying Agents and On-the-Fly Databases with Railway for Data Analysis

41:28 – The Dream of Autonomous Marketing

48:50 – Building API-First Products and Agent-Native Infrastructure

Key Points

GTM engineering has evolved from Clay-style data enrichment workflows into full-stack agent orchestration — where one person running multiple Claude Code agents can replace the output of a large team.

The practical setup starts with a single folder containing your environment file (API keys for every tool in your stack), transcription software like Super Whisper, and Claude Code.

Cody demonstrates running seven or more agents simultaneously across LinkedIn outreach, Facebook ad creation, cold email campaigns, Notion document generation, and live data dashboards.

Code-generated ad creative (React components exported as PNGs) costs nearly nothing to produce at scale and allows rapid testing of messaging variations before investing in polished visuals.

Deploying proven workflows to Railway turns one-off agent tasks into always-on, autonomous processes that run 24/7.

Domain expertise is the real multiplier — the vocabulary you bring from your field determines the quality of output you can extract from these tools.

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 CODY ON SOCIAL:

Cody’s startup: https://www.graphed.com/

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

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