The Startup Ideas Podcast

Get Your Complete Financial OS at https://startup-ideas-pod.link/brex_SIP In this solo episode, I break down how to make money with Meta Muse by mining Facebook groups, Marketplace, and Instagram to match buyers with sellers. The idea came after my wife and I had a baby and the agency we hired for a night nurse admitted it found candidates in Facebook groups. I demo Muse finding night nurses at $30 an hour in Montreal, show how to give it a phone number through Bland AI, and share the Marketplace and Instagram tactics I'd use. I close with my four-level framework for growing a matching side business into a trusted list, a community, and a vertical software company. Timestamps 00:00 – Intro 01:19 – Sponsor: Brex 03:22 – Meta Muse 03:52 – Why this is a big opportunity 04:33 – Picking a Niche, City, and Rules 05:44 – Owning the asset and data 06:51 – Cleaning Data You Own 08:27 – Lessons From eBay 10:37 – A Phone Number With Bland AI 11:30 – Undervalued Facebook Groups 12:55 – Business Pages and IG Niche Pages 13:46 – Prompting Muse Into Facebook Groups 15:19 – Mining FB Marketplace and Instagram 17:20 – Four Levels of Personal Agent Arbitrage 20:38 – Results From Montreal Groups 21:40– Goods vs. Services 22:15 – Muse vs. Dots and Instinct 24:09 – Closing Thoughts Key Points Muse mines public Facebook groups, Marketplace, and Instagram to match buyers and sellers in any niche and city. A clean list of names, needs, prices, and trust signals is an asset I own and can sell later. Facebook groups, business pages, and IG niche pages look undervalued as Muse agents start crawling them. I tell Muse where to look, and Facebook groups gave me the highest quality data. My four levels: mine the data, own the list, own the room, then build technology. A lean cost structure lets me charge a success fee of a few hundred dollars and keep high margins. 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/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

Gemini 4 Argon is LIVE, Google's most advanced AI model, try it for yourself: https://startup-ideas-pod.link/gemini4-argon In this solo episode, I share what I learned after a week with Gemini 4 Argon, Google's new frontier model. I show three places it shines: marketing with AI, physical AI, and complex engineering, with a real build for each. You'll see Argon rebuild my holding company's website, write a post in my voice, design a Shopify product customizer that exports STL files, and build a free quoting app for commercial cleaning companies. I close with six techniques that raise Argon's output quality and the Google AI plan I'd pick for building. Timestamps 00:00 – Intro 01:35 – What Gemini 4 Argon Does Well 03:09 – Rebuilding the Late Checkout Website 07:49 – Justine Moore's Apple Computer Museum 09:20 – Using Argon for writing 13:03 – Physical AI and Vibe Manufacturing 17:41 – Made-to-Order Shopify Store 19:01 – Building Boring SaaS 21:59 – Commercial Cleaning Quote App 25:12 – 6 techniques to get the most out of Argon 28:39 – Closing Thoughts Key Points Argon produces premium creative work once I give it reference images and a clear direction. A skill built from my posts, newsletters, and videos lets Argon write in my voice. Argon builds Shopify product customizers that export print-ready STL files. Free, focused rebuilds of boring legacy SaaS put me in front of likely buyers. Argon gets the engineering right on prompt one, and design takes a few more rounds. /plan, /subagents, and /goal give me the biggest jumps in output 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/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

In this solo episode, I walk through eight GitHub repos and AI skills that I test at night and on weekends. The list covers launch videos, social research, local voice and dubbing, editable decks, agent teams, lead-magnet tools, idea stress-testing, and design guidance. For each one, I explain what it does, how I'd set it up, and one concrete way to use it to make money or run a business more easily. Most are free to download, and you pay for the model you run them with. Timestamps 00:00 – Intro 01:25 – Brag: Launch Videos From a Link 04:50 – Agent Reach for Customer Research 07:46 – Voice Studio: Local AI Voice and Dubbing 10:41 – PPT Master: Editable Decks From Notes 12:59 – OpenRig: Coding Agents as a Team 14:57 – Free Tools Skill by Corey Haynes 17:25 – Grill Me Skill by Matt Pocock 19:31 – Impeccable: Design Guidance for Agents 23:03 – My Top Picks: Brag and Agent Reach 24:07 – Closing Thoughts GitHub Repo Links: Brag (launch video skill): https://startup-ideas-pod.link/brag Agent Reach: https://startup-ideas-pod.link/agent-reach Voice Studio: https://startup-ideas-pod.link/voice-studio PPT Master: https://startup-ideas-pod.link/ppt-master OpenRig: https://startup-ideas-pod.link/openrig Free Tool: https://startup-ideas-pod.link/marketing-skills Grill Me Skill: https://startup-ideas-pod.link/grill-me Impeccable Skill: https://startup-ideas-pod.link/impeccable Key Points Most of these tools are free to download, and the main cost is the model you run them with. Work I do for someone up front, like a launch video or a design fix, starts a conversation in place of a cold email. Posts on Reddit, X, and YouTube show tasks people still do by hand, and a small tool can fix them. Translated onboarding videos make a recurring monthly service for other companies. Grill Me logs my assumptions so I check them with a real customer before I build. Ten clients at $1,000 a month is $10,000 a month, and that cash flow funds the move from services to technology. 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/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

In this solo episode, I walk through the 13 businesses I think are left to build in the agentic era. I believe that in a world of agents and abundance, owning the right assets puts you in a better position. For each business, I explain what it is, why it holds up in a world of agents, and one or two examples, from Othership's sauna clubs to Osmo's window-cleaning robots. This episode gives you a map of where to build next, at any age. Timestamps 00:00 – Intro 01:15 – AI-Native Service Firms 02:57 – Offline Businesses 05:41 – Distribution and Organic Audiences 07:34 – Proprietary Data Sets 09:08 – Domain-Specific Harnesses 12:16 – Robotics and Physical AI 14:01 – Physical Products With a Fan Following 16:34 – Compute and Energy 17:31 – Health, Longevity, and Care 19:30 – Marketplaces and Social Networks 21:04 – Real Assets 21:46 – Vertical Agents 22:18 – Security 24:08 – Closing Thoughts Key Points In an AI-native service firm, agents do most of the delivery and a human reviews the exceptions. Ad clicks went from about five cents in 2009 to $1–5 today, so I start with organic audiences. Proprietary data and domain-specific harnesses are the two gold rushes I see right now. Offline businesses, niche hardware, and robotics sell rooms, objects, and labor that agents only support. Compute, energy, and security are the scarce inputs every agent depends on. 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/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

Google's most advanced audio models are LIVE, try them for yourself: Gemini 3.8 Live: https://startup-ideas-pod.link/gemini-3.8-live Gemini 3.5 Transcribe https://startup-ideas-pod.link/gemini-3.5-transcribe Gemini 3.5 Live Translate: https://startup-ideas-pod.link/gemini-3.5-live-translate In this episode, I talk with Vas from Varick about what it takes to put AI to work inside a real company. Vas makes the case that AI pays off through process reengineering, and he walks me through the exact method his forward deployed engineers (FDEs) use: interviews, process mining, step sorting, and agents built inside existing systems of record. We go through real engagements, including a $5B public software company and an accounts payable overhaul that cut the cost per invoice from $31 to $6. By the end, you get a clear picture of the FDE role, the business opportunity behind AI roll-ups, and a five-day plan to start on your own. Links Mentioned: FDE Presentation: https://startup-ideas-pod.link/FDE-slides Vas’s Article: https://startup-ideas-pod.link/vas-fde Timestamps 00:00 – Intro 01:31 – Sponsor: Google 03:58 – FDE Overview 05:02 – AI Roll-Ups and Process Reengineering 08:04 – The Personal Systems Analogy 09:55 – Understanding a company’s process (step-by-step) 14:11 – Case Study: $5B Software Company 18:11 – 4 Buckets for Every Step 19:04 – Build Inside Systems of Record 21:36 – Case Study: PE Portfolio 23:43 – Selling to C-Suite Executives 27:07 – Process of Mapping Five NetSuite Companies 28:39 – Example: Accounts Payable Process Map 32:54 – Case Study: 60-Person Accounting Firm 34:51 – When to Use Code, Agents, or Humans 36:00 – Choosing AI Models 38:16 – Sidekick vs Background Agents 40:29 – The 3 Skills of a Top FDE 42:25 – Why FDEs Earn So Much 45:26 – Five-Day Starter Plan 47:39 – On-Premise Hardware Demand 48:36 – OpenAI Private Intelligence 50:20 – The Full Playbook 52:01 – Closing Thoughts Key Points AI pays off when you re-engineer the process first, then build agents into it. Map the real process with interviews, system-of-record mining, and existing docs. Sort every step into four buckets: delete, plain code, agent, or human decision. Build agents inside the tools clients already use, like Salesforce, NetSuite, and Slack. Sell the outcome each buyer cares about, and prove it with before-and-after KPIs. Top FDEs combine domain knowledge, production engineering, AI judgment, and strong communication. 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/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND VAS ON SOCIAL Varick Agents: https://www.varickagents.com/#hero-section X/Twitter: https://x.com/vasuman AI Forward Deployed Engineers: https://learn.varickagents.com/fde-in-30-days

I watched almost 60 minutes of Sam Altman on stage at OpenAI Dev Day 2026. Out of 20-plus launches, I pick the three or four that I think can make people billions of dollars in aggregate. I break down Dots, OpenAI's personal agent platform, plus the Decisions API, the Agents API with computer use, and Sign in with ChatGPT. I also share my four-step framework for building in this world and two business ideas I hope someone takes. If you want to build a business and make money around AI, this episode is for you. Timestamps 00:00 – Intro 01:48 – Dots, the Personal Agent Platform 03:29 – OpenAI Doubles Down on Plugins 05:02 – Decisions API 06:43 – Agents API With Computer Use 08:01 – Sign In With ChatGPT 12:36 – Where should you build? 13:46 – Idea: Real-World Work APIs 15:04 – Idea: Analytics for Agent Discovery 17:05 –Closing Thoughts Key Points Dots gives plugin builders a front door to 1.2 billion weekly active users. OpenAI is doubling down on plugins to make ChatGPT the app ecosystem for AI. Sign in with ChatGPT lets users bring their current plan, so a free core app with paid upsells now works. Workflows too niche for OpenAI to build become viable businesses. The strongest businesses in this world own the trigger, the action, and the feedback. Two open opportunities: real-world work APIs and an analytics layer for agent discovery 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/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

In this solo episode, I break down the $5 trillion wave of small businesses set to change hands as their owners retire, and why AI agents make this wave a real opening for solo founders. I walk through how Thrive Holdings and General Catalyst buy accounting firms, property managers, and support centers, then run AI agents inside them to lift margins. Then I show how I'd run a one-person holding company: the folder structure, the agent files, the human approval rule, and my average week. I close with the strongest arguments against AI roll-ups, including the one I take most seriously. Timestamps 00:00 – Intro 01:52 – Why the $5 Trillion Shift Is Happening Now 04:52 – Examples: Thrive Holdings& General Catalyst 08:38 – The Fund Playbook 10:07 – The Small-Deal Gap 10:51 – The One-Person Holdco 14:23 – The Folder Structure 16:47 – The Agent Pipeline 18:38 – Inside a Reviewer Agent File 19:41 – My Week Running the Holdco 21:49 – How to Land the First Business 22:24 – Arguments Against AI Roll-Ups 27:43 – Closing Thoughts Key Points About a million small businesses, part of a $5 trillion shift, are set to sell by 2035 as owners retire (McKinsey). AI agents now handle the work these firms run on: data entry, document chasing, status updates, and first drafts. The big funds chase bigger deals, which leaves the small firms open for solo founders and small teams. A one-person holdco runs on shared agents and rules, plus a GM with real upside at each business. A person approves all agent work before it reaches a client. The corrections log, turned into rules every week, becomes the most valuable asset in the holdco. 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/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

In this solo episode, I break down the huge business opportunity because Meta has opened Muse, its personal AI agent, to developers. You can now submit a connector, which lets Muse use your service when someone asks it for help, and I think this could be the app store moment for AI. I explain how a connector works, share four startup ideas you can build on one, and cover how to get customers beyond Meta's directory. I also show how I'd build a first version with a coding agent like Claude Code or Codex, and what I'd test before submitting it to Meta for review. Timestamps 00:00 – Intro 01:35 – The App Store Parallel 04:55 – How a Muse Connector Works 07:29 – Startup Idea 1: Lead Gen for Business Suppliers 09:28 – Startup Idea 2: Home Repair Dispatch 11:07 – Startup Idea 3: Paddle Match and Court Finder 12:47 – Startup Idea 4: Family Dinner Planning 14:10 – How to pick an idea? 14:56 – How People Find Your Connector 15:32 – Growth Idea 1: Partner With Creators 16:17 – Growth Idea 2: Product-Led Sharing 17:46 – Growth Idea 3: Connected Marketplaces and Directory Placement 19:34 – Building the First Version 21:38 – Custom Connectors and Meta Approval 23:56 – Where to Start This Week 25:31 – Closing thoughts Muse Connector Prompt: https://startup-ideas-pod.link/muse-connector-prompt Key Points Meta has opened Muse to developers, and a connector lets Muse use your service when someone asks it for help. I look closely at the step in a request where someone needs a business that can deliver and money changes hands. With a small budget, I'd start with lead generation, because I can show a customer a sample before writing much software. I plan distribution around channels I can reach, such as creators, product-led sharing, and connected marketplaces. A coding agent can build the first version, and I still inspect the results and test the awkward requests myself. To start this week, I'd talk to one type of customer about the last time they dealt with the task. 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/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

Get Your Complete Financial OS at https://startup-ideas-pod.link/brex_SIP In this episode I speak with Nicholas Cole about the real value of everything you write on the internet. Cole has 15 years of experience as a nonfiction writer and ghostwriter, and he runs the SaaS platform Typeshare. He gives me a simple model with three tiers of content: commodity, personality, and original. He also explains the digital brain, which he calls your personal language model, and he shows how AI repeats your approved language at scale. By the end, you know how to judge the short, medium, and long-term value of a piece before you write it. Start Writing Online In 30 Days: https://startup-ideas-pod.link/ship30 Timestamps 00:00:00 – Intro 00:03:36 – POV is the Moat 00:05:40 – Language As Open Source 00:11:24 – Value Is Relative To The Reader 00:14:26 – Approved Language 00:17:43 – The Value of AI in Writing 00:22:09 – Commodity Ideas 00:24:23 – How To Set Up The System 00:27:58 – Ownership IS Association 00:33:56 – Build your Personal Data Set 00:39:53 – Branded Content vs Founder-Led Content 00:45:17 – What is Original Content? 00:46:35 – Writing Versus Short-Form Video 00:48:58 – Three Types of Hooks 00:49:50 – Timely Content vs Timeless Content 00:53:27 – Voice is 3 dialed settings 00:56:32 – Finding your Voice 01:00:49 – The Company Brain As The Moat 01:07:30 – Closing Thoughts Key Points A point of view creates the moat, because copycats must wait for your next idea. Content sits in three tiers: commodity, personality, and original. Each tier adds different leverage. Ownership equals association. Volume builds it, and personality details make it strong. Your life story is the unmade data set, so AI learns it only from your own writing. Every piece sits on a spectrum from timely to timeless, so match your expectations to the type. Humans do the thinking and the writing. Robots do the repeating and the remixing. 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/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND COLE ON SOCIAL X: https://x.com/Nicolascole77 Youtube: https://www.youtube.com/@nicolascole77 Ship 30 for 30: https://startup-ideas-pod.link/ship30

In this episode, I talk with Ryan Vogel about Jev, a new type of AI built for classification. Ryan shows how Jev takes an input plus an output schema and returns a probability for each choice in about 200 milliseconds. He demos Jev sorting 1,700 emails for 18 cents total, then covers lead scoring, support routing, video clipping, and browser control. I push him on the startup angle: find a business with an expensive queue of incoming information and put Jev at the front of it. You leave with a clear mental model, real use cases, and a simple way to try it today. Links Mentioned: Jev/Typeface AI: https://typesafe.ai AI Gateway: https://vercel.com/ai-gateway Timestamps 00:00 – Intro 02:27 – What Jev Is and Why It Matters 04:32 – Email Triage Demo 07:19 – Jev as an AI Decision Maker 15:46 – How to Use Jev in a Business 20:48 – Startup Idea: Local Services Matching and Instant Quotes 22:51 – Use Case 1: Bitcoin Signal Test and Limits 24:03 – Use Case 2: Auto-Clipping Long Videos 25:27 – Use Case 3: Browser Control: Flight Pick in 7.1 Seconds 26:18 – How to Get Access 27:25 – Closing Thoughts Key Points Jev is a classifier: an input and an output schema go in, and a probability for each choice comes out. Ryan's demo scores 1,700 emails for 18 cents total. Each Jev query takes about 200 milliseconds, whatever the input and output structure. Use Jev at any point where a business makes fast, repeatable decisions on incoming data. Keep Jev in an advisory role, and save frontier models for high-intelligence tasks like trading. Instant access runs through the Vercel Gateway, and a waitlist covers direct access. 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/ 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: https://x.com/ryanvogel Youtube: https://www.youtube.com/@vogeldev/videos

I sit down with Remy to go through Instinct, the new invite-only personal agent that runs inside iMessage. Remy shares his raw chat history on screen: a haircut booking in Copenhagen, a restaurant reservation, a Bali visa on arrival, and an Emirates Skywards sign-up. We cover the parts that impress us, the points where the agent hits a wall, and the privacy questions that stay open. By the end of this episode you understand what Instinct does today, and you get fresh ideas for personal agents in general. Timestamps 00:00 – Intro 02:08 – Instinct Pros 11:18 – Instinct Cons 13:16 – Simple Onboarding 15:19 – Tools and Connectors 17:43 – Example 1: Booking a Haircut in Copenhagen 20:24 – Example 2: Restaurant Booking and Calendar Entry 23:03 – Example 3: Bali Visa on Arrival and Emirates Skywards 26:00 – Closing Thoughts Key Points Instinct hides the agent complexity behind a phone number and iMessage, so a first-time user starts in seconds. Remy gets it to book a haircut, hold a restaurant table, file a Bali visa on arrival, and open an Emirates Skywards account. A spend-limited virtual card keeps the blast radius small when the agent pays for things. The agent stalls when a task needs a phone app or an Indonesian checkout page. Users report that Instinct keeps copies of email after they disconnect Google, so treat privacy as an open risk. The trusted person network lets one Instinct talk to another, which builds network effects into the agentic era. 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/ 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 AI with Remy: https://www.aiwithremy.com/

Get Your Complete Financial OS at https://startup-ideas-pod.link/brex_SIP I welcome Ras Mic back to the pod to explain the phrase "software factory." Mic shares his screen and walks through the exact system that he runs today. His factory has four steps: isolate, build, prove, and ship. He keeps the whole system in five or six markdown files, so it works with any model and any harness. By the end of this episode, you can boot up your own factory, run many agents in parallel, and trust the code that comes back. Create your own Software Factory: https://startup-ideas-pod.link/ras-software-factory Timestamps 00:00 – Intro 02:17 – Software Factory Definition 03:44 – Why the Software Factory Matters 05:23 – Step 1: Isolate With Git Work Trees 11:34 – Step 2: Build With the Code Structure Skill 14:48 – Step 3: Prove With Evidence-Driven Testing 22:25 – Step 4: Ship With Grep Loop and Greptile 26:52 – The Physical Factory Analogy 29:21 – A Software Factory Is Markdown Files 30:02 – Closing Thoughts Key Points A software factory is a workflow of skills and domain knowledge, so it runs with any model and any harness. Isolate: every feature starts in a fresh git work tree branched from origin main, so each agent keeps its own station. Build: a code structure skill makes the agent write service layer code that a human developer can read. Prove: the agent records a before state and an after state as video, screenshots, or numbers. Ship: Greptile scores the PR, and the agent loops back to build until it earns five out of five. Mic runs up to 15 features in parallel and reviews the visual proof instead of the raw code. 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/ 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

I talk with Ras Mic about GPT-6 Astra. We skip the game demos and the 3D toys, and we focus on use cases to earn money or improve products. I share 9 Astra prompts that I posted publicly, and Greg Brockman reposted. Ras then shows his hardware project: he moved from a speaker idea to a parts list, a Blender layout, and merged code in about 30 minutes. The takeaway is simple: use this model for the ideas that felt too large for you last year. Timestamps 00:00 – Intro 01:53 – Astra Overview 04:14 – 9 Astra Prompts 11:48 – Jarvis Speaker Idea 16:21 – Think Bigger with Astra 18:29 – Vibe Coding to Vibe Manufacturing 21:16 – Closing Thoughts Key Points Astra costs more per task, and it uses fewer steps, so the value per dollar stays high. A performance audit moved one of Ras’s apps from 800 ms to 20–30 ms. A security audit on his live payments app found real risks in production. Ras went from a speaker idea to a $561 parts order and a merged pull request in about 30 minutes. Ras’s point: intelligence keeps climbing, and bravery stays flat. Ask for bigger things. The shift that vibe coding brought to software now reaches physical products. 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/ 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

I run this episode solo. I explain local AI in plain terms: the model runs on hardware I control, and a cloud model runs somewhere else. I map the four pieces of the local AI landscape — the model, the warehouse, the software, and the workflow — and I define the words that beginners meet first: parameters, tokens, context window, quantization, and GGUF. I walk through the Google open model stack (Gemma 4, Google AI Edge, LiteRT-LM, AI Edge Gallery), compare the other open model families, and show three ways to run a model today. I close with a first workflow you can copy and three startup ideas that use local AI as the wedge. And a special thank you to Google for supporting the podcast. Timestamps 00:00 – Intro 01:35 – The Open Model the Landscape 03:09 – Vocab Decoder 06:48 – Google Gemma Clearly Explained 10:29 – Other Open Model Families 14:20 – Path 1: Run Gemma in LM Studio 18:17 – Path 2: Ollama 20:15 – Path 3: Google AI Edge 21:07 – Hardware Cheat Sheet 21:52 – First Workflow to Build 22:47 – Workflows Before Fine-Tuning 25:06 – Local vs Cloud vs Hybrid Eval 26:33 – Framework for Local AI Startup Ideas 27:22 – Startup Idea 1: Home Health QA Reviewer 29:24 – Startup Idea 2: Offline Field Report Copilot 32:10 – Startup Idea 3: Pre-Send Reviewer for Professional Services 34:47 – Build Your Local AI Lab 37:55 – Closing Thoughts Key Points Ask whether the model is good enough for the job, and the business opportunities become clear. Local AI has four pieces: the model, the warehouse (Hugging Face), the software (LM Studio or Ollama), and the workflow you build around them. Gemma 4 E4B is my practical starting point; E2B fits phones and older machines. Hybrid architecture wins: local does the private first pass, cloud does the heavy reasoning, and a human approves anything important. Start with one repeated workflow — one folder, one model, one output — and run it 10 times. I see a 24-month window to build local-AI-native software for verticals that still run early-2000s 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/

On this solo episode, I review five free, open source GitHub repos that help you build products, make money, or save time: Peter Yang's No AI Slop Skill, the CRM by TryComp AI, Video Use by browser use, SkillSpector by NVIDIA, and Phone Harness. For each repo I explain what it does, why it matters, how to install it, and the first small workflow to try. I close with a simple three-step method: install the repo, make one small workflow work, then decide to productize it or keep it as your own leverage. Timestamps 00:00 – Intro 01:44 – Repo 1: No AI Slop 05:20 – Repo 2: Agentic-first CRM 10:52 – Repo 3: Video Use 15:15 – Repo 4: SkillSpector 18:49 – Repo 5: Phone Harness 22:25 – Closing Thoughts Links to repos: petergyang/no-ai-slop — https://github.com/petergyang/no-ai-slop trycompai/crm — https://github.com/trycompai/crm browser-use/video-use — https://github.com/browser-use/video-use NVIDIA SkillSpector — https://github.com/NVIDIA/SkillSpector phone-harness — https://github.com/ShawnPana/phone-harness 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/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

In this solo episode I explain a role that I call the marketing engineer. I believe this person becomes one of the most valuable hires in tech in the next 18 to 24 months. I define the job, I show the four eras of marketing that lead to it, and I give the tool stack that makes it work. I use a commercial HVAC software company as a worked example, and I list six systems that a marketing engineer builds. I close with four ways to earn money from this skill and a 30-day plan to learn it. Timestamps: 00:00 – Intro 01:46 – The Evolution of Marketing 04:29 – What is a marketing engineer 07:19 – Build the Growth OS 10:18 – Marketing Engineer Tool stack 13:23 – Live Data Workflow 14:32 – Agent Job Description 16:56 – Example: vertical SaaS for HVAC contractors 18:27 – System 1: Customer Truth 20:20 – System 2 - 4: Founder content, Outbound signal and Creative Testing 23:31 – System 5: AI search visibility and the growth cockpit 24:19 – System 6: Eval Loop 25:06 – Ways to Monetize 29:41 – The 30-day plan 32:24 – Closing Thoughts Key Points I expect the marketing engineer to command salaries from 250K to more than 1 million dollars. I build the growth repo first, because it holds the marketing memory of the whole company. I write a job spec for each agent, in the same way that I write a job description for a person. I measure qualified replies and pipeline, because business results show the true signal. I treat taste and judgment as the moat, because agents become a commodity. I recommend one working system over five half-built ones. 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/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

Vinny is back on the pod, and he explains WebMCP. WebMCP puts MCP tools inside the browser UI, so any agent that you bring can read a page and act on it. Vinny demos an espresso gear store where his agent compares two machines, checks the counter width, matches accessories, adds an item to the cart, and applies a coupon. Together we map the agent-native options, from headless APIs to in-app agents, and we place WebMCP in the middle of that map. I close the episode with two cash-flowing business ideas that a semi-technical founder can start today. Timestamps 00:00 – Intro 02:28 – WebMCP Clearly Explained 07:22 – Demo: Conditional Tools And Browser Session Login 09:11 – The Agent-Native Paradigm 12:12 – How Agents Interact with Apps 16:05 – Demo: Accessories, Cart, And Coupons 18:42 – Where WebMCP fits 21:03 – Startup Idea 1: WebMCP Conversion Agency 24:27 – Startup Idea 2: Agent Mystery Shopper 26:11 – Closing Thoughts Key Points WebMCP makes a website agent-readable and agent-actionable through a short, clear list of tools. The browser session carries the login, so tools stay conditional and the setup stays simple. The live store demo compares machines, matches accessories, adds to the cart, and applies a coupon. WebMCP sits between headless APIs and in-app agents, and it keeps the visual UI that most people prefer. Vince says WebMCP launched in February as a joint Microsoft and Google experiment in Chrome. The first markets: complex commerce, SaaS admin consoles, read-only flows in regulated industries, and internal 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 VINNY ON SOCIAL: X/Twitter: https://x.com/hot_town Youtube: https://www.youtube.com/channel/UCHP5Hdx0X-sM0uv2bl_OOqg

On this week’s SIP Live we dive deep on the X timeline to give each post a sip or a skip, and we answer questions from the chat. This episode covers AI-native operations, a baby food app that earns a million dollars a month, iMessage agents, human customer support as a brand advantage, small teams, reading habits, and sales advice for builders. Listeners get concrete first steps, business ideas they can copy today, and two clear opinions on each take. Timestamps 00:00 – Intro 00:59 – Where an AI-Native Company Starts 04:48 – The $1M-a-Month Baby Food App 10:31 – An App Store for iMessage Agents 16:32 – Customer Support Is Eating Engineering 23:38 – RIP to the 2-Pizza Team 26:36 – Paul Graham on Reading as an Advantage 33:31 – The Worst Moves for 2026 37:01 – From Designer to Salesperson Key Points Make the company legible first: keep meeting notes and SOPs in files that an LLM can read. Let the team explore AI tools freely, then ask one department, such as admin, to lead. A million-dollar-a-month app leaves room for niche copies that earn 10K to 30K a month. Build loops that turn daily support transcripts into prototypes and measurable goals. Human support works as a marketing advantage, and premium buyers pay for it. Sales stays part of the owner's job, so learn the frameworks and start cold calling. 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 JONATHAN ON SOCIAL Unscheduled CEO Podcast: https://www.unscheduledceo.com/ X/Twitter: https://twitter.com/Jicecream LinkedIn: https://www.linkedin.com/in/jonathan-courtney-4510644b/

I sit down with Billy Howell for an inside look at a real business that runs on Grok Bot agent teams. Billy tests hundreds of tools for The Rundown, and he uses Grok Bot to run his local newsletter, The Arlington Bagel, which goes to 6,000 readers every Thursday. He walks me through his chief of staff bot, his research and sales agents, and the routines that move work forward while he sleeps. We cover the token math, the four-week plan he follows, and the business models that fit this tool best: newsletters, directories, and Shopify stores. You leave with a setup you can copy for one project this month. Timestamps 00:00 – Intro 02:22 – Grok Bot Overview 03:12 – What makes GrokBot special 06:09 – Pick one project 08:09 – Creating your initial agent team 11:19 – First week sprint with Grok Bot 13:06 – Automated Routines within Grok Bot 15:51 – Agent Org Structure 17:35 – Best Practices for building Agents 19:45 – Newsletter playbook 21:12 – Plugins 22:24 – Newsletter playbook pt 2 25:43 – Sales agent 29:06 – Iterating and Improving Agents 33:34 – Grok Bot business ideas 36:45 – Site stack builder 38:36 – Directories and landing pages 41:42 – One project for a month 42:50 – Closing Thoughts Key Points Keep one project per Grok Bot account, so context stays clean and tokens stay available. Start with a chief of staff bot, and let it audit your business and name the first three agents. Follow the four weeks: build the team, execute, hire and fire, then automate. Ask each agent for a five-line brief: what shipped, what is stuck, what needs you. Move repeatable steps into scripts or make com, and keep agent tokens for high value work. Newsletters and directories give the easiest entry, and the two models feed each other. 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 BILLY ON SOCIAL: X/Twitter: https://x.com/billyjhowell Instagram: https://www.instagram.com/billyjhowell Tiktok: https://www.tiktok.com/@saasfatigue Youtube: https://www.youtube.com/@billyjhowell

Get Your Complete Financial OS at https://startup-ideas-pod.link/brex_SIP I talk with Remy, known online as AI with Remy, about agent skills and how to share them across a whole team. Remy explains that a skill is an SOP for AI: a markdown file that teaches Claude your exact way to do a task. He then shows his system. He keeps his team skills in one GitHub repository, and he installs that repository as a plugin in Claude Code and Codex. The result is one source of truth, automatic updates for everyone, version control, and company ownership of the work. Listen to this episode if you run daily tasks with agents and you want your whole team to get the same quality of output. Remy’s prompt + skill setup: https://startup-ideas-pod.link/Remy-skills Timestamps 00:00 – Intro 02:33 – Skills as SOPs for AI 04:27 – How the agent uses skills 05:13 – AI is single player 06:21 – Example Skills: Notion, Brand Voice, Email 08:07 – How to share skills 11:40 – What is a Plugin? 14:58 – Anatomy of a Plugin 15:30 – Version Control and Rollback 16:58 – A Second Repo for Personal Skills 18:29 – The Day Claude Deleted 150 Skills 20:02 – Skills as Company Assets 21:00 – A Web App on Top of the Repo 24:46 – How Many Skills to Build 27:00 – The Self-Improvement Loop 28:15 – Skill Maxxing 30:40 – Closing Thoughts Key Points A skill is a markdown SOP that teaches an agent your exact way to do a task. Most skills sit on one laptop, so a great process stays with one person. A GitHub repo plus a plugin gives the team one source of truth for every skill. Auto-update sends each skill edit to every teammate in Claude Code and Codex. A company-owned repo keeps the skills when a teammate moves on. Remy builds thin agents and thick skills, so any harness can run the same process. 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 AI with Remy: https://www.aiwithremy.com/

Get Claude Code: https://startup-ideas-pod.link/claude-greg In this solo episode I lay out the exact system I use to turn Claude Code into what I call an AI employee. My premise is simple: give Claude the same things you would give a person joining your company (a workspace, memory, a brief, a clear ticket, eyes, review, a schedule, and permissions). I build the whole setup live around a real idea I found on ideabrowser.com , a missed-lead responder for med spas, and I share the specific prompts I use at each step. By the end, the product, the customer feedback, the docs, the demos, the reviews, and the recurring work all live inside one operating loop. I close with a seven-day plan you can run at your own pace. And thank you to Claude and Anthropic for supporting the podcast. Setup Claude Code to be your 24/7 employee: https://startup-ideas-pod.link/Claude-code Timestamps 00:00 – Intro: The AI Employee Map 05:47 – Step 1: Creating The Workspace In Claude Desktop 14:53 – Step 2: The Brief And Plan Mode 18:08 – Step 3: The Ticket And Defining Done 22:15 – Step 4: The Eyes And Desktop Preview 26:13 – Step 5: Review In Layers And The Diff View 29:34 – Step 6: The Schedule And Routines 34:32 – Step 7: Parallel Agents And Worktree Isolation 39:14 – Step 8: Permissions: Safe, Ask First, Human-Owned 41:15 – Step 9: Skills, Connectors, And Hooks 44:28 – The Seven-Day Plan 47:42 – Closing Thoughts Key Points I treat Claude Code like a new hire: workspace, memory, brief, ticket, eyes, review, schedule, permissions. The repo brain teaches Claude how I work and what good looks like. Plan mode comes first: Claude reads the context, proposes an approach, and waits for my approval. One ticket at a time means one task, one finish line, and one reviewable change. The eyes matter: Claude opens the app in desktop preview, clicks the flow, checks the console, and reports what a buyer experiences. Routines and permissions turn a chat tool into a 24/7 operator with clear boundaries. 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 Allie K. Miller to talk about the shift from managing AI agents to enabling them. Allie runs a workforce of 34 AI agents led by an AI chief of staff named Simon, plus six directors named after Friends characters. She shares her three-word prompt, her daily AI diary, her AI watchdogs, and her rule to build the factory before the product. We then debate the future of software: why enterprises still want a vendor to call, and why consumer software now rewards taste and distribution. Listeners leave with one mindset shift and a first step they can finish in under three hours. Get Your Complete Financial OS at https://startup-ideas-pod.link/brex_SIP Timestamps 00:00 – Intro 02:29 – Become a Great Agent Manager 04:49 – The Three-Word Prompt 08:12 – The Pyramid of Proactivity 12:23 – Making the Company Queryable 19:14 – How to Design an AI Workforce 22:25 – AI as a Watchdog 24:56 – Startup Opportunities 26:29 – Build the Factory, Then the Product 30:09 – The SaaS Question 34:53 – Consumer Software as Art 37:12 – High-Value Bottlenecks 44:56 – Closing Thoughts Key Points Allie sits three rungs above her 34 agents. She sets the infrastructure and waits for escalations. Her strongest prompt runs three words on top of full business context: do smart things. She holds the risk tier steady and expands only the breadth and scope of agent work. A dictated daily diary captures the context that lives outside meetings, email, and Slack. AI watchdogs remain wide open: duplicate work, calendar conflicts, and meeting disagreements. The bigger play is a software factory. Build the primitives once, then ship each product faster. I see opportunity on both sides of software: enterprises want a vendor to call, and consumers reward taste. 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 ALLIE ON SOCIAL X/Twitter: https://x.com/alliekmiller Instagram: https://www.instagram.com/alliekmiller/ LinkedIn: https://www.linkedin.com/in/alliekmiller/

In this solo episode I break down Cloudflare's AI agent announcement in plain English and explain why I see it as the new business model for the internet. I walk through AI crawl control, pay per crawl, the monetization gateway, and the x402 payment rail, and I show how a single request turns into a transaction. From there I give three startup ideas built directly on top of this shift: a niche data refinery, agent readiness for businesses, and expert archives turned into agent tools. For each idea I lay out the wedge, the first customer, the first version, and how I would sell it. My core claim: the internet is moving from pages humans visit to resources agents use, and the builders who move now own the doors. Timestamps 00:00 – Intro 01:01 – The Old Paradigm of the Internet 02:57 – The New Paradigm of the Internet 03:41 – What Cloudflare is actually doing 06:33 – An AI Index for all our customers 07:34 – The Agent Internet Stack 09:09 – Why Now Is the Best Time to Build 10:29 – Startup Idea 1: The Niche Data Refinery 17:10 – Startup Idea 2: Agent Readiness for Businesses 23:43 – Startup Idea 3: Expert Archives as Agent Tools 30:36 – The Filter for Finding Ideas 32:33 – Closing Thoughts Key Points The human web monetized attention; the agent web monetizes useful resources, priced per request. Cloudflare's pay per crawl, monetization gateway, and x402 turn the HTTP 402 status code into a live checkout at the edge. Idea 1: refine one niche's messy data into clean fuel for agents, starting with 100 businesses in one city. Idea 2: sell agent readiness by showing a founder exactly what AI says about their company today. Idea 3: package an expert's archive into one job-specific agent tool the audience already wants. Every one of these works as a manual services business today and productizes as agent payments mature. 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 bring Cody Schneider back on the show to build two marketing agents end to end, live. The first one monitors LinkedIn posts from creators in your category, scrapes everyone who engages, waterfalls those profiles into emails and phone numbers, and then runs cold email and LinkedIn DMs with an agent managing the replies. The second one turns internal conversations, sales calls, and podcast transcripts into a daily organic LinkedIn content engine across an entire team. Cody names every tool in the stack, shares the real infrastructure costs, and shows the actual terminal commands he runs in Claude Code. By the end you have two systems you can go set up today for your startup. Timestamps 00:00 – Intro 02:27 – Agent Number One: Cold Outbound Agent 04:26 – Finding Creators in Your Category on LinkedIn 09:09 – Apify Explained and the API Maestro Actors 10:59 – Extracting Engagers Live in Claude Code 12:59 – Agent Versus Automation 15:45 – Waterfall Enrichment: GitLeads, Apollo, Origami 17:13 – Compliance, Data Brokers, and What Stays Legal 21:40 – Waterfall Enrichment: Million Verifier and LeadMagic 25:38 – The Cold Outbound Infrastructure 28:33 – Software Factories and Marketing as Code 31:41 – Agent Number Two: The Organic LinkedIn Engine 39:34 – Earned Media Math at $22 CPM 42:31 – Closing Thoughts Key Points LinkedIn engagement is a hand raise, so it beats firmographics as a targeting signal. Ten to twenty source accounts give you roughly 80% surface area coverage of an industry. Waterfall enrichment moves cheapest to most expensive: GitLeads, then Apollo, then Origami or Prospeo. Roughly $200 a month covers sending software plus inboxes for about 10,000 cold emails. An agent here is plain code on a cron job with an LLM attached where judgment is needed. Organic content works best when it starts from real human source material like calls, Slack, and transcripts. 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

I go solo on this one to break down graph engineering, the term I keep seeing go viral on X. I define it in plain English: prompt engineering is how you ask AI a better question, context engineering is how you give AI better information, and graph engineering is how you design the work around the AI so it lives as a managed workflow instead of one giant chat. I walk through the vocabulary (jobs, arrows, state), separate knowledge graphs from agent graphs, and run a full worked example on whether to launch an AI bookkeeping product for Shopify merchants. Then I show three levels of implementation, from manual lanes on a whiteboard up to LangGraph and n8n, plus ready-made graphs for support, content, and code. You leave with a repeatable way to turn one AI workflow you already run into a map of steps, checks, handoffs, loops, and human approvals. Timestamps 00:00 – Intro 01:24 – Prompt Engineering, Context Engineering, Graph Engineering 02:50 – Chat vs Graph 03:35 – Defining Terms and Workflows 06:44 – Knowledge Graphs vs Agent Graphs 08:47 – When to use Graph Engineering 10:01 – Example: AI Bookkeeping For Shopify Merchants 13:22 – The Diamond Pattern Graph Visualized 15:10 – Three Levels of Implementation 17:14 – Customer Support Graph 18:45 – Content Creation Graph 19:30 – Coding Graph 20:42 – The Trap Of Oversized Graphs 22:22 – Building Your First Graph 24:53 – Closing Thoughts Key Points Graph engineering means designing the work around the AI: jobs connected by arrows, with shared state moving between them. Knowledge graphs help AI understand how information connects; agent graphs help AI understand how work should move. Reserve a graph for work with multiple steps, multiple sources, parallel paths, checks, risks, or approvals. Separate the writer from the checker, since a single model grading its own answer inflates confidence. Draw and run the graph manually first; add LangGraph, n8n, or Make com once the structure proves itself. Aim for the smallest graph that raises quality, and place the human gate where mistakes get expensive. 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 Vinny for a live tour of Buzz, an open source, agent-native chat app from Block built on an open protocol. Vinny makes the case that openness is the real story here: agents arrive as first-class teammates, the harness underneath each agent swaps freely between Claude Code, Codex, Goose, and open code, and your entire chat context travels with you through every swap. He demos real output, including a CRM app built with the Wasp full stack framework and deployed to Railway, plus a tweet leaderboard that pipes daily stats back into a channel through a public API. I press him for the honest state of the software and for the setup advice he actually uses day to day. By the end I share where I land on Buzz versus Slack, and why I think anyone building right now gains from putting their hands on tools like this. Timestamps 00:00 – Intro 02:57 – Agents as First-Class Team Members 03:49 – Swappable Harnesses Under Any Agent 06:55 – Audio Huddles With Agents 08:34 – Git, Feature Branches, and Parallel Worktrees 11:20 – Building a CRM App with Buzz and Agents 13:43 – Why This Matters 18:13 – Best way to engage with your Agents 23:53 – Shared Compute and Local Models 25:42 – Model Choice, Data Ownership, and Lock-In 27:36 – Context as the Foundation 29:39 – Setting Up Agents 31:21 – Skills and Speed 33:03 – Who Should Try Buzz Today 34:36 – My Take: Live in the Future 38:10– Closing Thoughts Key Points Buzz treats agents as members of your team, so one shared chat becomes the context layer for humans and agents together. The harness under any agent swaps freely, and every chat, project, and decision comes along for the ride. Globally installed agent skills stay available inside Buzz, so an existing Claude Code setup carries straight over. Agents branch, work in parallel worktrees, push to Git hosting on your own relay, and ship live apps end to end. Shared compute lets a small team run one local model on one machine and use it from many computers. Buzz sits in early preview today, which makes it a strong fit for solopreneurs and small teams iterating fast. 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 VINNY ON SOCIAL: X/Twitter: https://x.com/hot_town Youtube: https://www.youtube.com/channel/UCHP5Hdx0X-sM0uv2bl_OOqg

Cody Schneider is back on the podcast, and I ask him to lay out what a real marketing agent looks like once you get past the hype. He draws a hard line: an agent owns unified business data, runs on a cadence, and improves from the results it reads back. We use one concrete business as the sandbox — an AI-first product built on top of WordPress — and Cody walks the entire stack behind a Facebook ads agent that researches pain points, generates static and video creative, publishes through the Facebook Marketing API, kills the losers, and promotes the winners. You leave with a business idea, the exact infrastructure list, and the tools we use to run it today. Timestamps: 00:00 – Intro 01:54 – Defining a Marketing Agent 04:00 – Startup Idea: AI for WordPress 07:27 – AI-First Plugin Ideas: Yoast, WPForms, WooCommerce, Akismet 09:55 – The current state of Meta Ads 12:48 – Bundling the Stack and Choosing Channels 15:23 – Two creative pipelines: static and video 17:25 – The Data pipeline and warehouse 24:11 – Ad strategy 25:51 – Solving for Entropy 28:01 – Let the market pick the winner 34:26 – Closing Thoughts Key Points WordPress powers 43% of all indexed websites, which leaves a wide open lane for AI-first products built on that stack. A marketing agent earns the name when it owns live data, runs on a cadence, and learns from its own results. The infrastructure comes down to three pieces: a pipeline (Airbyte), a warehouse (ClickHouse), and cloud hosting (Heroku, Railway, or similar). Facebook's Andromeda algorithm reads your creative and your landing page, so the ad copy now carries the targeting. Fresh inputs — competitor ad libraries, YouTube transcripts, podcast transcripts — keep agent creative varied over time. Paid ads let you test a thousand angles and read the market's verdict inside 48 hours. 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

I welcome Ryan Carson back to the show to turn anyone into a world-class agent operator. Ryan spent 25 years founding companies, scaled Treehouse to around 110 employees and a million learners, and now runs Untangle, an AI divorce agent for family law firms, as a team of one while his revenue tracks toward 4x this month. He walks me through his full stack: running cloud agents in parallel, building automations that watch production and improve themselves, and shipping 22 to 40 PRs a day, often from his phone. We also get into keeping token costs sane through model routing and building a durable reputation by sharing your work on X. By the end, listeners hold a clear playbook to run agents, automate the busywork, and ship faster. Timestamps 00:00 – Intro 01:43 – The New Agent Paradigm 04:24 – Inside Ryan’s eight-screen desk setup 07:23 – Why Devin and cloud agents 10:00 – Everyone Is Now a Manager of Agents 13:15 – Local vs Virtual Development 14:50 – Agent Management System 24:22 – 3 Automations to build 32:54 – Self-improvement loop with Grace 34:38 – Token costs and model routing 40:33 – Building reputation on X 44:03 – Closing thoughts Key Points Ryan frames every knowledge worker as a manager of agents, and mastering that role is the new edge. Cloud VMs let Ryan run five to ten agents at once and ship 22 to 40 PRs a day. Roughly half of Ryan's work happens on his phone, which keeps his agents moving in real time. Automations like a production watchdog and a daily self-improvement loop hand Ryan a running summary of what matters. Model routing keeps costs in check: budget around $5k a month per employee and lean on cheaper fine-tuned models. Sharing your work publicly on X compounds into relationships and opportunities over time. Numbered Section Summaries Meet Ryan and Untangle Ryan Carson returns to walk me through his stack. He spent 25 years founding companies, scaled Treehouse to about 110 employees and a million learners, and now runs Untangle, an AI divorce agent for family law firms, solo, with revenue on track to 4x this month. The World-Class Agent Manager Ryan's core premise: everyone is now a manager of agents, and becoming the best in the world at it is the goal. He argues that managing agents makes you more technical over time, much like a skilled engineering manager. The Desk Setup and Key Security Ryan lays out eight screens, a 52-inch monitor, a UG Monk paper to-do system, a vertical mouse, and Whisperflow for voice. He stores all prod write keys in 1Password and hands them to agents only when a task truly calls for it, keeping production safe. Cloud Agents Over Local Machines Ryan explains cloud VMs: click a button, spin up a fresh environment, and run many agents in parallel while the code stays cleanly separated. He credits this shift for his 50x jump in output and urges builders to move work into the cloud as fast as they can. Staying Sane at 50x With 22 to 40 PRs a day, Ryan treats his job as making 10 to 20 high stakes decisions before lunch. He pins his most important threads, checks them on a roughly 25-minute cadence, and works from his phone so his agents keep moving. Automations That Run the Business Ryan shares three automations: an end-to-end signup test that browser-tests three times a week for about $60 in tokens, a 9am production watchdog that summarizes customer activity and links to real UI, and a daily self-improvement loop where an agent named Grace grades chats on a rubric and ships roughly three fixes a day. Token Costs and Model Routing After a $20k token month, Ryan settled on roughly $5k per employee plus heavy model routing, using cheaper fine-tuned models like SWE 1.7 for loop work. He favors independent agent labs such as Devin, AMP, Factory, and Cursor for affordable long-term engineering, while enjoying the subsidized Codex Mac app for everyday 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 RYAN ON SOCIAL Ryan’s Website: https://www.ryancarson.com X/Twitter: https://x.com/ryancarson Untangle: https://untangle.us

I sit down with Vas from Varick Agents to map out exactly how to break into AI forward deployed engineering — and how to grow into a sharper FDE — in thirty days. We start from a single premise: every company can now buy the same frontier intelligence, so the real advantage moves to deployment. Vas traces the role back to Palantir, explains the judgment that decides where AI belongs, and lays out the audit → evals → deployment loop that turns raw models into measurable business value. He then hands over a full 30-day plan to build, harden, measure, and defend a production-grade agent, so you can do the job before you hold the title. The whole conversation stays tactical and grounded, with clear examples I can apply today. The FDE Blueprint: https://startup-ideas-pod.link/fde-starter Timestamps 00:00 – Intro 02:03 – What is an FDE 04:09 – How Palantir Popularized FDEs 06:16 – Deciding Where Intelligence Belongs 11:26 – What FDEs Earn 14:59 – Two Kinds of Judgment: Communication and Engineering 17:38 – How the Work Really Gets Done 20:40 – Audit, Evaluation, Deployment 22:56 – Which LLM to Choose 27:36 – Audit: Finding the Workflow Worth Rebuilding 31:47 – Evals: Turn non-determinism into evidence 32:57 – Deployment: Build on Existing Systems 38:59 – The 30-Day Plan Begins 49:13 – Final Thoughts Key Points Intelligence is now commoditized, so the real edge lives in deployment — the job of the AI forward deployed engineer. Vas traces the FDE role to Palantir, where engineers embed on-site, learn workflows, and customize the ontology per client. The strongest FDEs blend deep technical skill with consulting-grade communication — the rare "art plus science" combination worth up to a million dollars a year. The FDE loop runs audit → evals → deployment, and each improved workflow makes the next one clearer. Vas condenses a year of learning into a 30-day plan: build an agent, harden it, make it measurable, then defend it like an FDE 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 VAS ON SOCIAL Varick Agents: https://www.varickagents.com/#hero-section X/Twitter: https://x.com/vasuman AI Forward Deployed Engineers: https://learn.varickagents.com/fde-in-30-days

In this episode I sit down with Corey Ganim to unpack what he calls the simplest way to earn money with AI in 2026: a $999 AI Tools Assessment for small business owners. Corey lays out his entire four-phase system, from a probing discovery call through an AI-assisted analysis in Claude, a stupid-simple client report, and a review call that turns roughly half of clients into implementation buyers. We walk through his full upsell menu, seven client-acquisition methods that run on zero capital and zero audience, and his AI Concierge retainer that earns him about $1,000 an hour. Anyone listening leaves with a copy-and-paste playbook they can adapt to their own city or industry. Checkout Corey’s AI Audit Template: https://startup-ideas-pod.link/ai_audit Timestamps 00:00 – Intro and the episode promise 03:48 – The full playbook preview 06:39 – Phase 1: the discovery call 08:27 – Phase 2: AI Analysis 12:11 – Phase 3: The Report 22:24 – Tool and Model Selection 23:39 – Phase 4: The Review Call 26:15 – The Upsell Services 35:52 – Finding Customers 49:16 – The AI Concierge retainer 58:46 – Final Thoughts Key Points The core offer is a $999, 45-minute AI Tools Assessment that prescribes 3–7 off-the-shelf tools, backed by a full-refund guarantee tied to finding at least five reclaimed hours per week. Fulfillment runs in four phases: discovery call, AI analysis in Claude, a templatized report, and a review call that converts about half of clients into implementation work. The report stays deliberately simple, with an executive summary, an effort-versus-impact matrix, quick wins, a four-day quick-start plan, and a clear ROI slide. The upsell menu spans process redesign, automation builds, knowledge systems, custom workflows, and full implementation, with lifetime value reaching $3K–$10K or more. Seven client-acquisition methods run on zero capital and zero audience, from local meetups and door knocking to agency partnerships and office hours. The AI Concierge retainer ($1,200–$2,000/month for two calls) creates recurring revenue at roughly a $1,000 hourly rate. 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 COREY ON SOCIAL Youtube: https://www.youtube.com/@coreyganim X/Twitter: https://x.com/coreyganim

I sit down with Elie Steinbock to unpack loop engineering and how to run a business on loops. We start with the roots of the idea in the lean startup and Toyota's manufacturing, then move into practical, copy-ready workflows for SEO, Facebook ads, and product feedback. Elie walks through a live Google Search Console example on Draft Fantasy and shows how to set up an SEO loop that runs once a month for years. The core promise for listeners: hand repeatable business work to an AI agent that measures an objective metric and improves over time. By the end, you know how loops work and how to launch your first one today. Timestamps 00:00 – Intro and episode promise 02:54 – What is Loop Engineering 06:51 – Loops with AI agents: build and verify 11:17 – Example of Loop: SEO as an objective-metric loop 15:29 – Setting up the SEO loop and tools 25:27 – Cost and token economics 29:05 – The Paid ads loop 33:10 – The product feedback loop 36:25 – A minimal viable loop for every channel 39:21 – Closing Thoughts Key Points Loop engineering means giving an agent a task, an objective metric, and a stop condition so it improves on a schedule. The lean startup and Toyota's build-measure-learn cycle map directly onto AI agents. An SEO loop connects to Google Search Console and Data for SEO, then pushes rankings up month over month. These loops run cheaply — often a few dollars per monthly run — which beats the cost of an agency. The same pattern extends to Facebook ads, and a product feedback loop stands as the ultimate version. Start small with a minimal viable loop tied to a clear metric like impressions or ten likes. Numbered Section Summaries The Promise of Running a Business on Loops I open by asking Elie what listeners will walk away with, and he frames the whole episode: use loops to automate SEO, ads, and more. We agree the aim is clear, copyable workflows people can launch today. Where Loop Engineering Comes From Elie traces the recent buzz to Boris from Claude Code and Peter Steinberger, plus a joking tweet from his friend Dimitro about software that builds itself. He grounds it in the lean startup's build-measure-learn cycle, which itself grew from Toyota's lean manufacturing. Loops With AI Agents: Build and Verify Elie explains the agent version: a build step paired with a verify step and a clear stop condition. He uses Inbox Zero's evals as an example, where the agent keeps adjusting the prompt or model until accuracy passes 90%. The SEO Loop We dig into SEO as the flagship example, where Google ranking serves as a clean, objective metric. Elie describes a loop that runs once a month, learns from the last run via a markdown memory file, and steadily climbs the rankings. Setting It Up on Real Data Elie shows his Draft Fantasy Search Console, connects the agent to Google Search Console and Data for SEO, and runs the loop live in Codex. He shares the Atom Eve prompt as a deeper template people can copy. Cost and Token Economics I raise Ross Mike's skepticism about loop buzz and token spend, and Elie makes the case that an SEO loop stays cheap — often under five dollars per monthly run. He adds that Max-plan users have plenty of headroom, while tight budgets suit cheaper open models like GLM 5.2. Ads, Product Feedback, and the Ultimate Loop We move to a Facebook ads loop that tests copy and creative variants, favoring a mix of human hooks and AI optimization. Then Elie describes the product feedback loop — reading customer feedback, analytics, and logs to prioritize and ship — as the closest thing to a business that builds itself. Starting Small We close on the minimal viable loop: begin with one channel and a modest, verifiable metric like impressions or ten likes, then let it compound. Elie and I agree that every part of a business could sit on a loop, and starting one today makes for a low-risk experiment. 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 ELIE ON SOCIAL Youtube: https://www.youtube.com/elie2222 X/Twitter: https://x.com/elie2222

In this episode I bring Nick Vasilescu, co-founder of Orgo, back on the show to unpack the buzz around Grok 4.5. Nick makes the case for treating Grok 4.5 as a genuine AI co-founder inside harnesses like Hermes and OpenClaw, and he proves it live: spinning up cloud computers, wiring in tools, and building a full startup from idea to landing page to outreach. We race Grok 4.5 against GPT 5.6 Sol, tour Nick's agent stack, and talk through the cost paradox of a model this fast and cheap. Listeners walk away with a concrete playbook for standing up their own always-on agent today. Get Nick’s Agent Template Stack: https://startup-ideas-pod.link/nicks-stack Timestamps 00:00 – Intro 01:40 – Why Grok 4.5 release matters 03:39 – Automation versus a co-founder 05:16 – Setting up Hermes and Grok 4.5 on Orgo 09:01 – Why Orgo to manage Agents 11:23 – Grok 4.5 Cost discussion 14:02 – Grok 4.5 Fast Execution and Unlock 16:20 – The Agent tool belt 19:13 – X MCP for trends 20:37 – vidIQ for outliers and thumbnails 22:11 – Finding new startup ideas 26:15 – Grok 4.5 versus GPT 5.6 Sol 30:56 – Ranking and Reviewing the startup ideas 34:06 – The AI agency opportunity 38:46 – Thumbnails over Telegram 40:10 – Reviewing AI Agency Landing Page 41:58 – Vertical MCPs and agent startups 43:36 – Skill graph and the offer 45:25 – Reviewing the Thumbnail Generated 46:42 – Email Outreach Campaign 48:07 – Reviewing Market Insight 1-Pager 50:45 – From a Camry to a Ferrari 52:12 – Reviewing Cold Email Outreach Sequence 53:22 – Closing thoughts Key Points Grok 4.5 delivers Opus 4.8-level intelligence at a fraction of the cost and roughly 10-15x the speed of Fable. I learn to treat the model as a co-founder by handing it email, a phone number, a debit card, memory, and every connector that matters. Nick runs agents on Orgo cloud computers so they stay online, textable, and ready around the clock. Live, Grok 4.5 builds a landing page in about 40 seconds and wins on design and copy for me over GPT 5.6 Sol. The stack ships from idea to website, offer, thumbnail, and cold-email sequence in a single session. Nick's take: costs keep dropping while speed and intelligence keep climbing, so building your agent now compounds overnight. 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 NICK ON SOCIAL Youtube: https://www.youtube.com/@nickvasiles Instagram: https://www.instagram.com/nickvasilescu/ Personal Website: https://www.nickvasilescu.com/

In this episode I sit down with Dan Shipper to see how he runs his work and personal life on OpenAI's Codex Desktop with the 5.6 model. He walks through his card-based email setup, daily feeds for his company and Slack, and the in-app browser that lets his agent collaborate with him inside tools like Proof. We build a small SaaS app live, called Turnaround, and use it to explore why maintenance is the real product in the AI era and where Codex-native software heads next. Along the way Dan shares his pirates-versus-architects framing, his approach to fine-tuning a copy-editing model, and the patterns — pulses, Mailroom, and router threads — that hold his system together. The throughline: pick one simple win, let context do the heavy lifting, and manage the system instead of running every task by hand. Learn how to get customers with AI Agents: https://startup-ideas-pod.link/GTM-agents-IB Timestamps 00:00 – Intro 01:16 – Codex and GPT-5.6 Overview 03:40 – Training your own model: the step after skills 04:49 – Automating Email, Slack, Meeting Notes with GPT-5.6 08:53 – Why GPT-5.6 sharpens the results 10:26 – The light bulb moment with Codex 15:05 – Building Turnaround live: a maintenance badge 18:00 – GPT-5.6 vs. Fable: A tier and S-plus tier 19:34 – LFG and goal: looping toward a finished build 24:28 – Huge Opportunity: Codex-native apps 29:33 – The design checkpoint and the "warm paper" quirk 31:32 – Local models 34:04 – From 70% to 100%: pirates and architects 37:22 – Mailroom: giving Codex its own email address 40:58 – Getting started: download, grant access, explore 43:07 – Record and Replay: turning tasks into skills 44:37 – Closing Thoughts: Start small and build over time Key Points Codex Desktop plus the 5.6 model runs as a full operating system for knowledge work — email, research, and building software from one surface. Context is the multiplier: an agent wired into your computer and the web turns every inbox and feed into cards with a clear next action. Maintenance is the real product in the AI era, now that anyone can one-shot a first version. Codex-native SaaS — software you and your agent share inside the in-app browser — opens a fresh category with healthier margins. A live build of Turnaround, a maintenance-status badge, reaches about 70% in one pass; an architect carries it the rest of the way. Start with one simple win, grow the system over time, and let curiosity lead the way in. 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 DAN ON SOCIAL X/Twitter: https://x.com/danshipper Youtube: https://www.youtube.com/@EveryInc/videos Every: https://every.to/

In this solo episode I lay out why I believe building agents is the new SaaS: software is shifting from helping you do the work to doing the work with you. I walk through a full playbook — find a niche, pick a workflow with a paycheck attached, shadow the human, spec the agent, build the minimum useful version, sell a pilot like labor, then productize the repeatable parts. I share live market examples like Slang AI for restaurants and Same Day for home services, plus pricing models and a distribution strategy built on workflow teardowns. I close with a 30-day, zero-to-100 plan for launching an agent-first business. This one is for anyone eager to build with AI or simply become more productive. Timestamps 00:00 – Intro 01:38 – Building Agents is the new SaaS 04:11 – Pick a valuable workflow 06:12 – Shadow the Human First 09:34 – Build the Minimum Useful Agent 12:50 – The wrapper makes it SaaS 15:50 – Sell the Pilot Like Labor (and Pricing) 18:37 – Own the workflow 21:45 – The Zero-to-100 Plan in 30 Days 24:14 – Closing Thoughts Key Points Agent SaaS sells work as a service; the product is the job itself, priced like labor. Start with a workflow that already carries a paycheck: high frequency, clear finish line, existing software, learnable edge cases, and felt pain. Shadow a human across 10–20 real jobs before you write a single prompt — the detail is the product. Ship the minimum useful agent — draft-and-approve, triage, coordinator, or bounded action — and earn autonomy over time. The wrapper (logs, approvals, evals, analytics) creates trust and turns automation into real SaaS. Win distribution with workflow teardowns: show the old way, show the agent way, sell the painkiller. 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/

In this solo episode, I lay out the six skills I believe stay valuable as AI grows more capable. I chose these six because each one is open to anyone, each one starts this weekend, and each one rises in value as AI improves. I walk through agents and local models, distribution, robotics, curation, the builder distributor, and IRL community building, with one concrete first rep for every skill. My goal is to hand you one simple, clear map of where the world is heading and exactly how to begin. Timestamps 00:00 – Intro 00:57 – Skill 1: Running AI Agents and Local Models 04:51 – Skill 2: Marketers Who Build Distribution 09:03 – Skill 3: Robotics Engineers Who Build and Source Hardware 14:29 – Skill 4: Curators Who Yap and Make Short-Form Video 19:05 – Skill 5: The Builder Distributor 23:11 – Skill 6: IRL Community Builders 27:34 – Build Your Skill Stack Key Points I chose these six skills because each one rises in value as AI improves. Skill 1 is the grown-up version of prompt engineering: I design an AI worker with context, tools, memory, permissions, and a goal. Distribution beats posting, so I learn where attention already lives and turn it into trust before I sell. Hardware is the new frontier: cheap arms, open-source robot learning, and supplier sourcing put robotics within my reach. As the builder distributor, I ship the product and win the attention in one loop, which makes the one-person company real. Real rooms grow scarce and valuable, so I build belonging, trust, and context as my edge. Numbered Section Summaries The Premise: What Stays Valuable as AI Improves I open by picturing a near future where AI builds and writes almost anything, then ask which skills hold their value. I narrow it to six skills that anyone can start this weekend, each one climbing in value as AI gets better. Skill 1 — Agents and Local Models I describe the move from typing prompts to designing a small AI employee with context, tools, permissions, memory, a goal, and a way to check its own work. I add local models with tools like Ollama and LM Studio so you learn which jobs want a giant brain and which jobs want a reliable worker, and I suggest building a daily briefing agent with three sources as your first rep. Skill 2 — Marketers Who Build Distribution I explain that distribution runs far deeper than posting: it means knowing where attention already lives and the exact words people use to describe their problem. The winning marketer becomes part researcher, storyteller, media operator, and community builder, and the first rep is a distribution map plus 20 hooks for a single idea. Skill 3 — Robotics Engineers Who Build and Source Hardware I share my big insight: the last decade rewarded moving pixels, and the next decade rewards moving atoms too. With cheap cameras, low-cost arms like the SO-100 / SO-101, open-source work like Hugging Face LeRobot, and small VLA models, I suggest assembling a low-cost arm, teaching it one boring task, documenting every failure, and learning supplier sourcing on Alibaba. Skill 4 — Curators Who Yap and Make Short-Form Video I cover the curator who watches the timeline and says "this matters because…," translating new models, launches, and news for a specific niche. The algorithms reward raw, authentic yapping that carries a real take, and my rep is a seven-day curation sprint paired with a taste file of hooks, analogies, and titles you love. Skill 5 — The Builder Distributor I make the case that AI compresses the old build-versus-sell split into one person who prototypes the product, writes the launch thread, records the demo, DMs the first users, and iterates. The loop is the whole game, and my rep is a 48-hour loop: build the smallest version of one problem, then create 10 pieces of distribution before you feel ready. Skill 6 — IRL Community Builders I close with the old-school skill that grows more valuable as work moves to agents and feeds: real rooms full of ambitious people. Scarcity moves toward belonging, trust, and context, so I suggest hosting six to eight people around one sharp question and sending a recap that turns the room into a network 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/

In this episode I sit down with Amir to get tactical about running local AI models as part of a daily workflow. We center on GLM 5.2 from ZAI, how it stacks up against frontier models like Opus 4.8, and how a fusion approach lets you sequence a heavy thinking model with a lighter execution model for the best output at the lowest cost. Amir walks through setup in Cursor and Codex via OpenRouter, shares real token-cost math, and demos GLM 5.2 refining a live app. By the end you will know how to start today, where local models shine, and how model chaining keeps spend in check. Timestamps 00:00 – Intro 02:09 – GLM 5.2 and Z AI 04:01 – Specs: 1M context and Terminal Bench 2.1 05:22 – Making sense of benchmark scores 06:42 – Setup in Cursor or Codex with OpenRouter 10:18 – Local model upside: buy a machine, run tasks 11:42 – Token cost: 44 cents versus $2.38 13:36 – Future-proofing with an upfront hardware bet & The Uber subsidy analogy 16:49 – Model chaining and the vision workaround 19:23 – Token maxing vs routing tasks to the right model 20:54 – Answering the "cost is irrelevant" crowd 21:59 – Closing thoughts Key Points GLM 5.2 ships with a 1M-token context window and scores 81 on Terminal Bench 2.1, landing about four points behind Opus 4.8. A fusion approach (a term OpenRouter coined) sequences models: plan with Opus, execute with GLM 5.2, review with Composer 2.5 or Codex 5.5. Running GLM 5.2 in the cloud through OpenRouter costs roughly 44 cents for a task that runs about $2.38 on Opus 4.8 — close to a 5X saving. You can start today with credit-based access: load $20 in OpenRouter and route tasks to the right model. For images, Amir uses Opus 4.8 to read screenshots and describe them, then hands the layout to GLM 5.2 to act on. Teams are shifting from token-maxing to output-maxing, making model governance and chaining the smart play 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 AMIR ON SOCIAL Humblytics: https://humblytics.com/?via=community X/Twitter: https://x.com/amirmxt Youtube: https://www.youtube.com/@amirmxt

In this episode I sit down with George Lampropoulos, a 19-year-old founder who turns AI-built mobile apps into real revenue. George walks through his framework for reaching $10K a month—roughly $333 a day—starting with a simple, sellable idea you actually care about and ending with a distribution plan anyone can run. He shares the numbers behind WrestleAI (100K-plus downloads and close to $200K in revenue) and explains why a sharp "gotcha feature" and a clean Instagram funnel do most of the heavy lifting. We also dig into closing influencers, hiring a VA, running paid ads, and reading the metrics that decide whether you grow. If you want a practical, founder-tested playbook for building apps with AI, this one delivers. George’s $10K/mo app playbook: https://startup-ideas-pod.link/George-app-playbook Timestamps 00:00 – Intro 01:54 – George's track record with WrestleAI 02:36 – How AI unlocks fresh app ideas 06:29 – Reverse-engineering a viral idea from your feed 16:16 – Designing the UI/UX of the app 17:49 – The gotcha feature that sells the app 21:25 – Onboarding that converts 23:04 – Actionable Plan to $10k/mo 28:55 – Outreach as a numbers game 33:35 – Paid ads clearly explained 36:20 – Reading metrics: conversion, ARPU, retention 38:30 – TLDR: a great product earns inbound creators 39:51 – Answering the vibe-coding skeptics 39:51 – Scaling with vibe-coded app 43:35 – Why now is the app-building boom 46:05 – Closing Thoughts Key Points I learn why a simple, sellable idea you're passionate about beats pure distribution every time George breaks down the "gotcha feature"—one feature so clear that five seconds explains the whole app We cover a clean Instagram page that doubles as a sales funnel and as social proof for recruiting creators George shares his influencer playbook: lead with relationships, close on a call, and aim for a $2 CPM I get his paid-ads starter method—5 to 15 creatives, $100 a day, then keep the winners. George explains the metrics that matter early: conversion rate, a $2 ARPU target, and retention 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 GEORGE ON SOCIAL X/Twitter: https://x.com/GeorgeLampro20

In this solo episode, I walk through the implications of the ban of Claude Fable 5 — the most powerful model on the planet and the one I planned to build with — after the US government sent Anthropic a letter. I make the case for local AI by walking through the benefits: intelligence that lives on your own hardware, stays private, runs free after the hardware cost, and keeps working through bans, outages, and price hikes. I lay out the exact order I'd learn it in — runtimes, model-to-hardware matching, quantization, and agents — and I name the specific tools and models I reach for. Then I hand you five startup ideas that exist precisely because intelligence now sits on your desk. The payoff for you is a clear plan to own a resilient layer of your stack starting this week. Timestamps 00:00 – Intro 01:20 – The Fable 5 Ban 02:31 – Renting Access vs. Owning Intelligence 03:41 – How a Local Model Works 07:19 – The Local Model Stack 08:45 – Match Model to Machine 10:45 – Pick Your Model (Qwen 3, DeepSeek, Gemma, Llama) 13:09 – Quantization Explained 14:36 –The Local Agent Loop 17:45 – Model Routing (The Real Skill) 18:44 – Five Startup Ideas for the Local-AI Era 22:17 – Closing Thoughts Key Points One government letter took Fable 5 offline overnight, which is why I now own a private layer of my stack. Local models already handle roughly 80% of everyday ChatGPT or Claude tasks, fully offline and free after hardware. I'd learn it in order: runtime first (LM Studio or Ollama), then match model size to your RAM. A 12-billion-parameter model on 16 GB of RAM is the sweet spot where most people should live. Quantization (look for Q4) roughly halves the memory a model needs while keeping quality high. Pointing an agent like Hermes at a local model turns your desk into a private, always-on mini data center. 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/

Get my Fable 5 prompt pack: https://startup-ideas-pod.link/fable5-prompt-pack In this episode I break down how to get the most out of Fable 5, the most powerful model I've ever used. I move past the benchmarks and go straight into tactical use cases, copy-and-paste prompts, and startup ideas you can build today. I walk through tournaments for copy and landing pages, an interview-before-build workflow that hunts for product-market fit, and ways to point Fable at contracts, churn data, and years of your own notes. I close with three of my favorite startup ideas — a synthetic focus group firm, 48-hour custom software, and a contract refund firm — plus the exact prompts behind each. My goal here stays simple: leave you ready to build and earn with Fable 5 while it remains included in your plan. Timestamps 00:00 – Intro 02:22 – Anthropic Employee Edits a Launch Video With Fable 05:50 – Building an AI Content Engine 07:30 – Best way to configure Fable 5 08:42 – Prompt 1: Copywriting Tournament for Landing Pages 13:18 – Prompt 2: The Interview-Before-Build Prompt 18:34 – Prompt 3: Hire Fable to Kill Your Company 20:18 – Prompt 4: Your One-Page Operating Manual 21:20 – Prompt 5: Find the Gaps Worth Filling 22:06 – Prompt 6: Negotiation Simulator 23:11 – Prompt 7: The 80-Page Second Opinion on Contracts 24:56 – Prompt 8: Make Fable Build Its Own Tools 25:47 – Startup Ideas 31:23 – Closing Thoughts Key Points I show why low effort is the alpha, since Fable Low beats Opus High on routine work. I run tournaments — landing pages and ad copy scored by AI judge panels — to ship far stronger output. I use an interview-before-build prompt so Fable pushes back and writes specs with real product-market-fit odds. I point Fable at big datasets — contracts, churn data, support tickets, years of notes — to surface money and patterns. I share startup ideas Fable 5 makes viable today, including a synthetic focus group firm and a contract refund firm. 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/

S/o Coderabbit for sponsoring today’s vid: https://startup-ideas-pod.link/code-rabbit On this episode I sit down with Professor Ras Mic to break down agentic loops. We define what a loop is, explain why well-known builders like Boris and Peter swear by them, and stay honest about who they truly serve. Mic argues that human-in-the-loop remains the strongest setup today, and he walks through the one loop he runs every day for code review using Cursor, GitHub, and Greptile. By the end you will know when a loop earns its place and when your own hand belongs on the wheel. Timestamps 00:00 – Intro 01:23 – What is a Loop 07:59 – /goal Explained 11:32 – The Slop Machine 12:42 – Code Review as a use case for Agentic Loop 18:19 – Honest Take for Builders 20:42 – The Future of Loops 21:50 – Closing Thoughts Key Points A loop fires once from a human, then the agent generates, reviews its own result, and feeds it back to keep building. Human-in-the-loop keeps you directing, governing, and approving each step while the agent builds. Wide-open loops make heavy assumptions and burn serious tokens; Michael cites Peter's tweet about $1.3 million worth of tokens in one month. Reserve slash goal and similar loops for the $200/month plan, since the $20 and $100 tiers burn through fast. Loops shine in confined, fixed-feedback work: code review, SEO pages, and other binary tasks. Mic’s daily win is a closed code-review loop with Cursor, GitHub, and Greptile that chases a 5/5 score. 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

Become an AI Native Organization: https://startup-ideas-pod.link/ai-native-org In this episode I sit down with Theo to unpack what becoming AI native truly means. We define an AI native org as people managing agents, agents reading and writing to the company, and the company growing smarter over time. Theo opens his actual workflows, walking through a working prototype, an auto-generated client proposal microsite, and a live usability test that synthesizes feedback into a V2 in one session. We close with service-business startup ideas built on this same system, plus a free consultation offer for larger companies. For founders and operators, the value lands as a concrete playbook for turning speed into customer signal and a durable moat. Timestamps: 00:00 – Intro 04:09 – The Demis Hassabis origin story 06:53 – Defining AI Native Organization 08:19 – Mapping the system: people, agents, context 09:18 – Why people lead: strategy, taste, trust 13:23 – Agents: models using tools in a loop 16:12 – Evals and defining "good" 17:34 – Skill chains explained 20:06 – Proposal skill-chain demo setup 25:48 – Proposal microsite walkthrough 30:46 – Building the Daily Blitz feature demo 32:50 – Context as the foundational layer 41:07 – Daily Blitz ships and the labs page 43:47 – Bootstrapping context with a small team 46:21 – Usability test and live feedback 51:18 – Startup ideas: productize the system 54:28 – Closing Thoughts Key Points An AI native org runs on three layers: people for judgment, agents for execution, and context as the shared brain. Everyone becomes a manager, so I set each agent up with a clear goal, the right skills, tools, and context. Skill chains fire playbooks back to back, lifting quality and keeping outputs grounded in real data. A living context layer gives agents 2020 vision, letting a personalized proposal ship in minutes. Live prototypes plus built-in usability tests turn raw ideas into customer signal the same day. The fastest service play right now: niche down by industry, function, and company size, then sell this system. 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 THEO ON SOCIAL X/Twitter: https://x.com/TheoTabah LinkedIn: https://www.linkedin.com/in/theotabah/ LCA: https://www.latecheckout.agency/

In this episode, I sit down with Alex Finn for a full, screen-shared walkthrough of Hermes Desktop, the new desktop home for the Hermes AI agent. I open with a clear challenge: by the end, sell me on installing Hermes Desktop, show me real ways to make money and stay productive, and explain his move from OpenClaw. Alex tours every major surface — sessions, profiles, artifacts, skills, cron jobs, and sub-agents — and shares money-saving tactics at each step. We close on the idea that matters most to me: aiming these agents at other people's challenges as the clearest path to real value. Timestamps 00:00 – Intro 04:04 – Sessions and Context Management 06:10 – Profiles Explained 08:49 – Model-Based vs Role-Based Profiles 12:58 – Artifacts as a Second Brain 14:32 – Why Alex Switched From OpenClaw 17:32 – Skills, Tools, and Tool Sets 19:19 – Messaging and Cron Setup 21:44 – Reverse Prompting and the Brain Dump 28:09 – Sub-Agents vs Profiles 32:12 – Putting It Together: Solving Challenges 32:38 – The Daily Business Opportunity Scan 37:05 – Local Models: Mac Studio vs DGX Spark 39:03 – Reframing Cost as Investment 41:59 – The Real Way to Make Money With Hermes 42:51 – Closing Thoughts Key Points Hermes Desktop pulls sessions, profiles, artifacts, skills, and cron jobs into one polished, Apple-style interface. Smart session and context management keeps each message slim and keeps monthly costs low. Profiles map to different models — Opus 4.8 for strategy, ChatGPT 5.5 for coding, a local Qwen model for free research — so each task runs on its best fit. Reverse prompting plus a personal brain dump produces far stronger prompts, cron jobs, and outputs. Sub-agents handle one skill across many parallel tasks; profiles handle work where each step needs a distinct skill set. The biggest opportunity: aim your agent at Reddit and X to surface real problems you are positioned to solve. 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 ALEX ON SOCIAL Youtube: https://www.youtube.com/@AlexFinnOfficial/videos X/Twitter: https://x.com/AlexFinnX Creator Buddy: https://www.creatorbuddy.io/

In this solo episode I walk through Codex Sites end to end, building a real internal tool live so you can copy the exact workflow. I open by comparing Codex Sites with one-prompt tools like Replit and Lovable, then construct a Startup Ideas OS board in six prompts. Along the way I cover memory and persistent storage, safe actions, Codex skills, save-gates, and proving the loop so the app updates autonomously. The core promise: by the end you know how to ship a Codex Site that an agent keeps operating for you. This one suits builders who already live in Codex and want self-updating products. Timestamps 00:00 – Intro and Episode Agenda 01:17 – Codex Sites vs Replit and Lovable 04:33 – The Build Plan: Startup Ideas OS 05:08 – Prompt 1: Build the Shell with Sites 07:02 – Plugins Worth Using and Game Studio 08:54 – First Board Review 09:21 – Prompt 2: Add Memory and Show the Data Model 10:56 – Prompt 3: Create Safe Actions 13:25 – Prompt 4: Create the Startup Ideas Admin Skill 14:51 – Prompt 5: Save-Gate and Checkpoints 16:29 – Prompt 6: Prove the Loop from a New Chat 18:10 – Publish, Auth, and Live Updates 20:28 – TLDR: Memory, Safe Actions, Skills 22:40 – The Real Unlock and Closing Thoughts Key Points Codex Sites rewards builders who already live in Codex by updating apps autonomously after launch. Replit, Lovable, and Bolt stay the simpler one-prompt choice; Codex Sites trades that for autonomy and self-updating products. Out of the box you prompt in auth, databases, payments, email, analytics, and a secrets vault yourself. I build a Startup Ideas OS board in six prompts: shell, memory, safe actions, a skill, a save-gate, and a proof loop. Safe actions let an agent call approved buttons and named mutations, so edits flow from any chat. The real payoff is autonomous products that Codex keeps operating and improving on a live URL. 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/

In this solo episode, I break down the shift from a human-first internet to an agent-first one, where AI agents become the customers that discover, evaluate, pay, and recommend. I map the agent buying journey and the new infrastructure agents need: identity, tools, an inbox, memory, a wallet, and receipts. I ground it with concrete examples like AgentMail and Stripe's agent wallet, then show how to make your website agent-readable through structured docs, schemas, MCP tools, and executable actions. I close with rapid-fire startup ideas and my big prediction for the next ten years: build startups for agents. Timestamps: 00:00 – Intro 00:49 – The tweet: build startups for agents 01:47 – Old web vs. agent web 02:24 – The agent buying journey 04:38 – What agents need: identity, tools, inbox, memory, wallet, receipts 05:30 – Examples: AgentMail, Stripe agent wallet, support, procurement, MCP, travel agent 08:24 – Building an agent-readable website 09:31 – What does this change for Startups 11:55 – Rapid-fire startup ideas for agents 13:07 – Closing Thoughts Key Points I explain that AI agents are becoming the primary customers online, with agent traffic set to outnumber human traffic. I lay out the agent buying journey: finding, evaluating, transacting, using tools, and recommending to other agents. I list what agents need beyond what humans need: identity, tools, an inbox, memory, a wallet, and receipts. I walk through real examples like AgentMail, Stripe's agent wallet, support, procurement, MCP servers, and a travel agent. I show how to make a site agent-readable with structured docs, schemas, MCP tools, SDKs, OAuth, checkout, sandboxes, and receipts. I share rapid-fire startup ideas for the agentic era and frame my big prediction: build startups for agents. 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/

Live from Google I/O, I sit down with Logan Kilpatrick from the Google DeepMind team to unpack everything Google just announced and what it means for founders, developers, and anyone trying to build with AI right now. We dig into Gemini 3.5 Flash, the new Gemini Omni world model, the expanded Antigravity ecosystem, managed agents in the Gemini API, and the native Android app builder inside AI Studio. Logan breaks down how distillation is pushing Pro-level intelligence into Flash, where the biggest opportunities are for solo founders, and why the agentic era has finally moved from impressive demos to genuinely useful products. Thanks to Google for flying me out to Google I/O and making this conversation possible. Timestamps 00:00 – Intro 00:53 – Gemini 3.5 Flash: The New Workhorse Model 01:49 – How Flash 3.5 Stacks Up Against Sonnet 02:38 – Gemini Omni: A World Model for Any Input and Output 06:18 – Building a Content and Creator Layer on Omni 08:21 – What to look forward to 10:53 – Google Spark and Managed Agents 14:00 – The Agentic Era and Requests for Startups 17:17 – The Antigravity Ecosystem Overhaul 18:51 – AI Studio vs. Antigravity: Vibe Coding vs. Agentic Engineering 21:31 – Native Android Apps Built Inside AI Studio 23:44 – Closing Thoughts Key Points Gemini 3.5 Flash ships as a Sonnet-level workhorse model tuned for long-running agentic tasks, coding, and tool use, available on day one to 900M+ Gemini app users. Gemini Omni is a single model that takes any input and produces any output across video, image, audio, and music, fusing Veo, Nano Banana, Lyria, and TTS into one system. Managed agents in the Gemini API let builders ship agentic products with a single API call, using skills and markdown instead of writing orchestration code. The Antigravity suite now spans an IDE, agent manager, CLI, SDK, and API surface, all sharing the same agent harness that powers Gemini Spark. AI Studio targets vibe coding and now builds native Android apps for free, while Antigravity targets production-quality, million-line-codebase engineering. The cost of intelligence keeps dropping thanks to distillation, opening up smaller markets that previously needed a 40-person team and venture funding to address. 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 LOGAN ON SOCIAL X/Twitter: https://x.com/OfficialLoganK Youtube: https://www.youtube.com/@LoganKilpatrickYT LinkedIn: https://www.linkedin.com/in/logankilpatrick/