Learn what a marketing agent really is and how to build two systems: a signal-based LinkedIn cold outbound agent and a conversation-to-LinkedIn content engine that turns internal talks into organic growth.

Marketing Agents Masterclass: Build Two AI Growth Systems

Marketing Agents Masterclass (GROW your startup)

– Intro

– Agent Number One: Cold Outbound Agent

– Finding Creators in Your Category on LinkedIn

– Apify Explained and the API Maestro Actors

– Extracting Engagers Live in Claude Code

– Agent Versus Automation

– Waterfall Enrichment: GitLeads, Apollo, Origami

– Compliance, Data Brokers, and What Stays Legal

– Waterfall Enrichment: Million Verifier and LeadMagic

– The Cold Outbound Infrastructure

– Software Factories and Marketing as Code

– Agent Number Two: The Organic LinkedIn Engine

– The Power of Earned Media

– Closing Thoughts

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 – The Power of Earned Media
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.

Numbered Section Summaries

1. Signal-Based Outbound: Cody opens with the state of cold email: reply rates are down across every channel as AI-generated volume floods inboxes. His answer is to target intent signals instead of static firmographics, using LinkedIn likes and comments as evidence that a person cares about a specific topic right now.
    
2. Sourcing the Right Creators: He walks through picking 10 to 20 creators and company pages whose audience matches your customer, using his own feed as the filter. A handful of outliers in any niche capture most of the relevant audience, so chasing full coverage adds cost with thin marginal return.
    
3. Scraping Engagers with Apify and Claude Code: Apify is the scraping API layer: one key, endpoints for LinkedIn, Twitter, and more. Cody uses API Maestro's LinkedIn actors for profile posts, post reactions, and post comments, then runs a script in Claude Code that takes a post URL and returns deduped engager profiles. In the demo he pulls 63 raw engagers from a single post.

4. Waterfall Enrichment and Verification: LinkedIn URLs go to GitLeads first, then Apollo, then Origami or Prospeo for whatever remains, with LeadMagic added for mobile numbers. Everything then passes through Million Verifier so only valid addresses get sent to, which protects deliverability. His worked example: 50 profiles yields 32 emails at the first step, 10 more at the second, and the rest continue down the chain

5. Inbox Infrastructure and Cost: Cody buys burner domains and inboxes through Hypertide, Inbox Kit, or Instantly so cold sending stays fully separated from the company domain. He keeps four lanes apart: cold email, email marketing, transactional, and the domain the team runs the business on. Cost lands around $100 a month for infrastructure covering roughly 10,000 sends, plus Instantly's $97 tier.

6. The Organic Content Engine: For teams who prefer inbound, Cody interviews each teammate weekly, extracts insights from the transcript, drafts posts with an LLM, and schedules them to individual LinkedIn accounts through Ordinal. Ordinal's analytics flow back to the agent so winning topics get remixed, which mirrors what a great social media manager always did: prospect ideas, publish, read the data, and recycle proven posts on a 90-day cadence.

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

Learn what truly counts as a marketing agent: unified business data, recurring decision-making, and a feedback loop that improves results. Plus, how to apply the idea to ads and AI-first WordPress growth.

Build a Real Marketing Agent: Data, Cadence, and Feedback Loops

Marketing Agents Are Too Good Now

– Defining a Marketing Agent

– Startup Idea: AI for WordPress

– AI-First Plugin Ideas: Yoast, WPForms, WooCommerce, Akismet

– The current state of Meta Ads

– Bundling the Stack and Choosing Channels

– Two creative pipelines: static and video

– The Data pipeline and warehouse

– Ad strategy

– Solving for Entropy

– Let the market pick the winner

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.

Numbered Section Summaries
1. What Counts as a Marketing Agent Cody sets the bar at three things: unified data across the whole pipeline, autonomous decisions on a cadence, and a thinking loop that reads results back. In practice that means a data pipeline, a data warehouse, and code hosted in the cloud — a definition that separates a working agent from a linear automation workflow.

2. AI for WordPress as the Sandbox The pitch is simple: a vibe-coding layer for WordPress that bundles the plugins people normally stitch together — forms, CRM, security — into one package sold on tokens, with a $29-per-month base tier. Cody sees a $10 million a year company inside roughly 24 months for a team that executes and positions it well.

3. Rebuilding Proven Plugins as AI-First Products I share my agency days migrating time.com and techcrunch.com onto WordPress, and the absurd money moving through the plugin ecosystem. That leads to a repeatable play: find plugins with validated demand, then ship the AI-first version — an agent that writes the meta and internal links in place of Yoast's red and green dots, a conversational form that qualifies leads, a WooCommerce storekeeper writing product descriptions and abandoned cart flows.

4. Creative Research That Seeds the Ads Cody runs Perplexity against Reddit to pull the real pain points and desired outcomes of WordPress site owners, then ranks them by how often people reference them. The top three — the plugin layer, performance and speed, and security and maintenance — become the ad angles, and he reads out the exact script shape that comes from them.

5. Generating Creative at Volume Statics come from Kai AI with Google Nano Banana, seeded by an example ad from a competitor or a tangential industry, with a vision model reviewing every output against brand style guides for fonts, colors, and readable text. Video runs on HeyGen for avatar UGC today, with Seedance coming up fast — clips cap out around nine seconds, so longer spots need frame stitching.

6. The Data Stack Behind the Agent Airbyte pipes Facebook ads, Google Analytics, CRM, and Stripe data into a ClickHouse warehouse so the agent sees the full path from ad to revenue. Cody keeps Facebook Marketing API usage to writes only — publish, pause, promote — because the account bans people blame on agents trace back to TOS violations from pulling hundreds of millions of rows.

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

Learn how AI agents like Claude use context windows and harnesses, why large always-on instruction files can waste tokens, and how Claude skills use progressive disclosure to produce consistent outcomes.

AI Agent Context & Claude Skills: How to Get Reliable Results

How AI agents & Claude skills work (Clearly Explained)

– The Models Are Good Now

– How Context Windows Actually Work

– The Power of Skills

– How to create Skills

– Skill Maxxing

– What you need too build a project

– Recursively Building and Improving Skills

– Context Window Management and Token Efficiency

I sit down with Ras Mic to break down how AI agents actually work and why most people are using them wrong. Ras Mic explains the mechanics of context windows, makes the case that agent md files are largely unnecessary, and shares his step-by-step methodology for building custom skills that make agents dramatically more productive. Whether you're coding with Claude Code or automating workflows with OpenClaw, this episode gives you the foundational knowledge to stop wasting tokens and start getting real results from your AI tools.

Timestamps
00:00 – Intro
00:42 – The Models Are Good Now
01:20 – How Context Windows Actually Work
04:55 – The Power of Skills
09:17 – How to create Skills
16:35 – Skill Maxxing
19:05 – What you need too build a project
20:40 – Recursively Building and Improving Skills
29:23 – Context Window Management and Token Efficiency
33:02 – Closing Thoughts

Key Points

* The models (Opus 4.6, GPT 5.4) are exceptionally good now — the differentiator is the context and harness you build around them.
* Agent md and claude md files get loaded into context on every single turn, burning tokens and degrading performance as the context window fills up. 95% of users can skip them entirely.
* Skills use progressive disclosure: only the name and description sit in context until the agent determines it needs the full file, saving thousands of tokens per conversation.
* The best way to create a skill is to walk through the workflow with the agent step by step, achieve a successful run, and then have the agent write the skill based on that real context.
* Recursively refine skills by feeding failures back into the agent and having it update the skill file so the same mistake is avoided going forward.
* Scale for productivity by starting with one agent and building up workflows before adding sub-agents — start simple, then expand.

Numbered Section Summaries

1. The Models Are Good — Context Is What Matters

Ras Mic opens by declaring that the current generation of models, Opus 4.6 and GPT 5.4, are exceptionally capable. The conversation is no longer about which model is "better" in a general sense. What matters now is the quality of context you feed them — that is what separates quality output from slop.

2. How Context Windows Work

Ras Mic walks through the anatomy of a context window: system prompt, agent.md files, skills, tools, the codebase, and the user conversation. All of these stack up as tokens, and the window has a hard limit (around 250,000 tokens). When you hit that limit, agents compact — and performance drops. Understanding this structure is the foundation for everything else in the episode.

3. Skills and Progressive Disclosure

Skills solve the token-bloat problem. A skill file contains a name, description, and the detailed instructions — but only the name and description are loaded into context. The agent reads the full file only when it determines the skill is relevant. This means a skill costs roughly 53 tokens per turn versus 944+ for an equivalent agent.md file.

4. Building Skills the Right Way

Ras Mic shares his methodology: identify a workflow, walk through it with the agent step by step, correct mistakes in real time, and only create the skill after you have completed a successful run. He illustrates this with his sponsor email screening agent — the first attempt returned all-positive results because the agent had no criteria for rejection.

5. Recursively Improving Skills

Even after a skill is created, the agent will still hit edge cases and fail. Ras Mic treats each failure as an opportunity: identify the error, have the agent fix it, then tell the agent to update the skill so the failure is documented. After five iterations of this loop on his YouTube analytics report generator, the agent now executes flawlessly across eight data sources in about ten minutes.

6. Scaling for Productivity Over Flash

Ras Mic started with a single agent handling everything — email, spreadsheets, research. Only after building reliable skills did he add sub-agents for marketing, business, and personal tasks. He argues that jumping straight to multi-agent architectures (or adopting tools like Paperclip without building foundational workflows first) optimizes for what looks cool rather than what is productive.

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

Learn how AI agents work in the observe-think-act loop, what you need for a working setup (model, tools, context, harness), and how agents differ from chat to deliver real results.

Beginner’s Guide to Building Practical AI Agents

Building AI Agents that actually work (Full Course)

– Agents vs Chat

– The Agent Loop

– How Agents work

– Demoing Agents (Claude Code, Codex, Antigravity)

– Security and Agent Permissions

– Comparing Results Across Three Platforms

– Startup Idea: Cold Email Website Offer

– Folder Structure and Department-Based Agents

– Onboarding an Agent Like a Real Employee

– Voice-to-Text With Monologue and WhisperFlow

– Chat Memory vs. Agent Memory

– Building the agents md

– Context Engineering Over Prompt Engineering

– How Memory Compounds and Reduces Errors

– How Big Can memory md Get?

– Connecting Tools via MCP (Model Context Protocol)

– Working in Claude Code for High-Value Tasks

– Why the Real Value Is in Stacking, Not Summarizing

– What Are Skills? (SOPs for AI)

– Creating Skills

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

– Chaining Skills together

– Real-World Example: Automated Car Search

– OpenClaw and Migrating Agents to More Autonomous Platforms

– Which Platform Should Beginners Start With?

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

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

Timestamps
00:00 – Intro
01:35 – Agents vs Chat
03:22 – The Agent Loop
05:46 – How Agents work
06:39 – Demoing Agents (Claude Code, Codex, Antigravity)
08:52 – Security and Agent Permissions
10:43 – Comparing Results Across Three Platforms
13:57 – Startup Idea: Cold Email Website Offer
14:50 – Folder Structure and Department-Based Agents
15:52 – Onboarding an Agent Like a Real Employee
17:05 – Voice-to-Text With Monologue and WhisperFlow
18:04 – Chat Memory vs. Agent Memory
19:34 – Building the agents md
22:20 – Context Engineering Over Prompt Engineering
24:29 – How Memory Compounds and Reduces Errors
30:27 – How Big Can memory md Get?
31:43 – Connecting Tools via MCP (Model Context Protocol)
34:49 – Working in Claude Code for High-Value Tasks
37:09 – Why the Real Value Is in Stacking, Not Summarizing
40:04 – What Are Skills? (SOPs for AI)
43:08 – Creating Skills
48:36 – Real-World Example: Ads Analyst Skill: 4-Hour Process in Minutes
50:37 – Chaining Skills together
52:01 – Real-World Example: Automated Car Search
53:34 – OpenClaw and Migrating Agents to More Autonomous Platforms
55:19 – Which Platform Should Beginners Start With?
56:28 – Global vs. Project-Level Skills, Context, and MCPs

Key Points

* Agent platforms (Claude Code, Codex, Cowork, Antigravity, Manus, OpenClaw) are all running the same observe-think-act loop under the hood — learning one means you can use any of them.
* The shift from chat to agents requires moving from prompt engineering to context engineering: load the agent with rich context so simple prompts produce excellent results.
* A memory md file creates a self-improving loop where the agent learns preferences across sessions and makes fewer errors over time.
* MCP (Model Context Protocol), built by Anthropic, acts as a universal translator between your agent and every tool it needs — Gmail, Calendar, Stripe, Notion, and more.
* Skills are reusable SOPs packaged as markdown files; once you explain a process once, you can invoke it repeatedly, and they compound as you add three to five per week.
* Scheduled tasks turn skills into automated workflows — morning briefs, car searches, ad library analyses — that run on a cron without any manual trigger.

Numbered Section Summaries

1. The Agent Loop in Action

Remy kicks off with a live demo, sending the same prompt — "build a minimalist portfolio site for Greg Isenberg" — to Claude Code, Codex, and Antigravity simultaneously. All three platforms run the same observe-think-act loop: research the subject, write the code, spin up a preview, and verify the result with a screenshot. The demo makes it tangible that every agent harness is just a different car with the same engine.

2. Onboarding Your Agent Like a Real Employee

Remy shows that without context, an agent asked to "write me a cold email" has no idea who you are or what you sell. The fix is an agents.md (or Claude.md) file — a persistent context document loaded at the start of every session. You fill it with your role, business details, tools, and working preferences, and the result is that a two-word prompt produces a fully informed output.

3. Memory That Compounds

Chat models store memory invisibly in the cloud; agents require you to build it intentionally. Remy adds a memory.md file and a simple instruction in the context file: "When I correct you or you learn something new, update memory.md." Preferences like tone, email sign-offs, and design choices persist across sessions, and errors decrease over time.


The #1 tool to find startup ideas/trends - https://www.ideabrowser.com/

LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/

The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/

FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/

FIND REMY ON SOCIAL
X:https://x.com/remy_gaskell
Youtube: https://www.youtube.com/@aiwithremy
Instagram: https://www.instagram.com/aiwithremy/

Learn what Model Context Protocol (MCP) is, why it matters, and how it helps AI assistants connect reliably to tools and external services using shared integration patterns.

MCP for AI: How It Standardizes Tool Connections

Model Context Protocol (MCP), clearly explained (why it matters)

- Intro

- The Evolution of LLMs: From Text Prediction to Tool Use

- MCPs explained

- MCP Ecosystem Overview

- Technical Challenges of MCP

- Conclusion on MCP's Potential

- Startup Ideas for Developers and Non-Technical Users

For startup ideas, trends and prompts to build them join https://Ideabrowser.com....I’m joined by Ras Mic to explain Model Context Protocol (MCP). Mic breaks down how MCPs essentially standardize  how LLMs connect with external tools and services. While LLMs alone can only predict text, connecting them to tools makes them more capable, but this integration has been cumbersome. MCPs create a unified layer that translates between LLMs and services, making it easier to build more powerful AI assistants.

Timestamps:
00:00 - Intro
02:26 - The Evolution of LLMs: From Text Prediction to Tool Use
07:39 - MCPs explained
10:59 - MCP Ecosystem Overview
13:47 - Technical Challenges of MCP
15:05 - Conclusion on MCP's Potential
15:48 - Startup Ideas for Developers and Non-Technical Users

Key Points:

• MCP (Model ContextProtocol) is a standard that creates a unified layer between LLMs and external services/tools
• LLMs by themselves are limited to text prediction and cannot perform meaningful tasks without tools
• MCP solves the problem of connecting multiple tools to LLMs by creating a standardized communication protocol
• The MCP ecosystem consists of clients (like Tempo, Windsurf, Cursor), the protocol, servers, and services

1) What are MCPs and why should you care?

MCPs are NOT some complex physics theory - they're simply STANDARDS that help LLMs connect to external tools and services.

Think of them as universal translators between AI models and the tools they need to be truly useful.

This is HUGE for making AI assistants actually capable!

2) The Evolution of LLMs: From Text Prediction to Tool Use

Stage 1: Basic LLMs can only predict text
• Ask ChatGPT to send an email? "Sorry, I can't do that"
• They're glorified text predictors (if I say "My big fat Greek..." it knows "wedding" comes next)
• Limited to answering questions, not DOING things

3) The Current State: LLMs + Tools

Stage 2: LLMs connected to tools
• Companies like Perplexity connect LLMs to search engines
• This makes them more useful but creates problems
• Each tool = different "language" the LLM must learn
• Connecting multiple tools = engineering NIGHTMARE

This is why we don't have Jarvis-level assistants yet! 

4) Enter MCPs: The Game-Changer

MCPs create a UNIFIED LAYER between LLMs and external services.

Instead of your AI speaking 10 different "languages" to use 10 different tools, MCPs translate everything into ONE language.

Result? LLMs can easily access databases, APIs, and services without massive engineering headaches.

5) The MCP Ecosystem Explained

The MCP system has 4 key components:

• MCP Client: User-facing apps like @tempoai, Windsurf, Cursor
• Protocol: The standardized communication method
• MCP Server: Translates between client and services
• Service: The actual tool (database, search engine, etc.)

Brilliant move by Anthropic: SERVICES must build MCP servers!

6) Why This Matters For Builders

For technical folks:
• Opportunity to build tools like MCP app stores
• Easier integration between services
• Less engineering headaches

For non-technical folks:
• Watch closely as standards evolve
• When standards finalize, new business opportunities will emerge
• Think of MCPs as Lego pieces you'll stack to build powerful AI apps

Notable Quotes:

"LLMs by themselves are incapable of doing anything meaningful... The only thing an LLM in its current state is good at is predicting the next text." - Ross Mike

"Think of every tool that I have to connect to make my LLM valuable as a different language... MCP, you can consider it to be a layer between your LLM and the services and the tools." - Ross Mike

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/

BoringAds — ads agency that will build you profitable ad campaigns http://boringads.com/

BoringMarketing — SEO agency and tools to get your organic customers http://boringmarketing.com/

Startup Empire — a membership for builders who want to build cash-flowing businesses https://www.startupempire.co

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/rasmickyy
Youtube: https://www.youtube.com/@rasmic

A practical tutorial on using ChatGPT Operator to browse marketplaces, log product deals, test arbitrage workflows, request vendor quotes, and source samples—plus what worked and what got stuck.

ChatGPT Operator Tests for Arbitrage & Vendor Outreach

ChatGPT Operator Built a $500/Day Business in 30 Minutes (tutorial)

-  First Test: Automated Arbitrage

- Second test: Wedding Planning Automation

- Third Test: Chinese product sourcing

- Conclusion and Future Outlook

In this episode, Chris and Greg push the limits of ChatGPT Operator AI, testing its power in business automation, e-commerce arbitrage, and AI-driven deal sourcing. They break down real-world use cases, discuss AI's limitations, and reveal the future of automation in online business. If you're looking for AI side hustle ideas, automation tools, or e-commerce flipping strategies, this episode is a must-watch!

Timestamps:
00:00 - Intro
01:35 -  First Test: Automated Arbitrage  
11:22 - Second test: Wedding Planning Automation
21:53 - Third Test: Chinese product sourcing
30:31 - Conclusion and Future Outlook

Key Points:
• Demonstration of Operator AI ($200/month) for automating business tasks and market research
• Using AI to find price arbitrage opportunities on Facebook Marketplace and eBay
• Automating wedding vendor inquiries and catering quotes
• Testing product sourcing automation through Chinese e-commerce sites

1) First example: AUTOMATED ARBITRAGE 

They tested finding underpriced Ooni pizza ovens on Facebook Marketplace to flip on eBay.

• AI searches listings within 500 miles
• Automatically spots price discrepancies
• Creates spreadsheets of opportunities
• Can message sellers automatically

2) The arbitrage potential is HUGE 

• Could build 6-7 figure automated business
• Works across ANY product category
• Leverages price differences between platforms
• Fully automated sourcing process
• Only $200/month for the AI tool

3) Second test: WEDDING PLANNING AUTOMATION 

Had AI contact multiple caterers simultaneously:
• Automatically fills out contact forms
• Sets reasonable dates
• Manages follow-ups
• Creates instant price comparisons

Pro tip: Tell the AI to "move faster" - it actually works! 

4) Third insight: PRODUCT SOURCING 

Attempted to automate:
• Finding trending products on Chinese marketplaces
• Requesting free samples
• Creating accounts
• Managing vendor communications

Key learning: Some platforms block AI access!

5) BIGGEST TAKEAWAYS 

• $200/month seems expensive but ROI potential is massive
• Early adopters will have huge advantages
• AI can replace virtual assistants for many tasks
• It's learning and improving in real-time
• Can run 24/7 autonomous operations

6) PRO TIPS FOR USING OPERATOR: 

• Be direct and clear with commands
• Treat it like a smart but literal assistant
• Don't wait for it to be "perfect"
• Start simple and scale up
• Document what works

We're living in the future and most people don't even realize it.

The arbitrage opportunities with AI are MASSIVE right now.

Notable Quotes:
"I think this is one of the greatest arbitrage moments in business that we've ever seen." - Greg
"It's one thing to hear what Sam Altman says about how close we are to AGI... but then to see it just kind of go rogue, like just start Googling things, figuring stuff out... it's kind of breaking my frame." - Chris

Special Notes:
• The AI demonstrates learning behavior, similar to human trial and error
• $200/month subscription fee discussed as potentially undervalued given capabilities
• Multiple instances of Operator can be run simultaneously for scaled operations

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/

BoringAds — ads agency that will build you profitable ad campaigns http://boringads.com/

BoringMarketing — SEO agency and tools to get your organic customers http://boringmarketing.com/

Startup Empire - a membership for builders who want to build cash-flowing businesses https://www.startupempire.co

FIND ME ON SOCIAL

X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/

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The Koerner Office: https://tkopod.com
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Youtube: https://www.youtube.com/@thekoerneroffice/videos

I share startup ideas, AI workflows, and practical lessons on building products, finding customers, and growing a business with a small team.

Greg Isenberg

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Greg Isenberg

Greg Isenberg

Archive

AI Agents

Marketing Agents Masterclass (GROW your startup)

AI Agents

Marketing Agents Are Too Good Now

AI Models & Tools

How AI agents & Claude skills work (Clearly Explained)

AI Agents

Building AI Agents that actually work (Full Course)

AI Models & Tools

Model Context Protocol (MCP), clearly explained (why it matters)

AI Agents

ChatGPT Operator Built a $500/Day Business in 30 Minutes (tutorial)

Greg Isenberg