The Startup Ideas Podcast

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