The Startup Ideas Podcast

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

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

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.

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