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

1. 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.
2. 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.
3. 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%.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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

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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/

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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/