Making $$$ as a Marketing Engineer

In this solo episode I explain a role that I call the marketing engineer. I believe this person becomes one of the most valuable hires in tech in the next 18 to 24 months. I define the job, I show the four eras of marketing that lead to it, and I give the tool stack that makes it work. I use a commercial HVAC software company as a worked example, and I list six systems that a marketing engineer builds. I close with four ways to earn money from this skill and a 30-day plan to learn it. Timestamps: 00:00 – Intro 01:46 – The Evolution of Marketing 04:29 – What is a marketing engineer 07:19 – Build the Growth OS 10:18 – Marketing Engineer Tool stack 13:23 – Live Data Workflow 14:32 – Agent Job Description 16:56 – Example: vertical SaaS for HVAC contractors 18:27 – System 1: Customer Truth 20:20 – System 2 - 4: Founder content, Outbound signal and Creative Testing 23:31 – System 5: AI search visibility and the growth cockpit 24:19 – System 6: Eval Loop 25:06 – Ways to Monetize 29:41 – The 30-day plan 32:24 – Closing Thoughts Key Points I expect the marketing engineer to command salaries from 250K to more than 1 million dollars. I build the growth repo first, because it holds the marketing memory of the whole company. I write a job spec for each agent, in the same way that I write a job description for a person. I measure qualified replies and pipeline, because business results show the true signal. I treat taste and judgment as the moat, because agents become a commodity. I recommend one working system over five half-built ones. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

Vinny is back on the pod, and he explains WebMCP. WebMCP puts MCP tools inside the browser UI, so any agent that you bring can read a page and act on it. Vinny demos an espresso gear store where his agent compares two machines, checks the counter width, matches accessories, adds an item to the cart, and applies a coupon. Together we map the agent-native options, from headless APIs to in-app agents, and we place WebMCP in the middle of that map. I close the episode with two cash-flowing business ideas that a semi-technical founder can start today. Timestamps 00:00 – Intro 02:28 – WebMCP Clearly Explained 07:22 – Demo: Conditional Tools And Browser Session Login 09:11 – The Agent-Native Paradigm 12:12 – How Agents Interact with Apps 16:05 – Demo: Accessories, Cart, And Coupons 18:42 – Where WebMCP fits 21:03 – Startup Idea 1: WebMCP Conversion Agency 24:27 – Startup Idea 2: Agent Mystery Shopper 26:11 – Closing Thoughts Key Points WebMCP makes a website agent-readable and agent-actionable through a short, clear list of tools. The browser session carries the login, so tools stay conditional and the setup stays simple. The live store demo compares machines, matches accessories, adds to the cart, and applies a coupon. WebMCP sits between headless APIs and in-app agents, and it keeps the visual UI that most people prefer. Vince says WebMCP launched in February as a joint Microsoft and Google experiment in Chrome. The first markets: complex commerce, SaaS admin consoles, read-only flows in regulated industries, and internal tools. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND VINNY ON SOCIAL: X/Twitter: https://x.com/hot_town Youtube: https://www.youtube.com/channel/UCHP5Hdx0X-sM0uv2bl_OOqg

On this week’s SIP Live we dive deep on the X timeline to give each post a sip or a skip, and we answer questions from the chat. This episode covers AI-native operations, a baby food app that earns a million dollars a month, iMessage agents, human customer support as a brand advantage, small teams, reading habits, and sales advice for builders. Listeners get concrete first steps, business ideas they can copy today, and two clear opinions on each take. Timestamps 00:00 – Intro 00:59 – Where an AI-Native Company Starts 04:48 – The $1M-a-Month Baby Food App 10:31 – An App Store for iMessage Agents 16:32 – Customer Support Is Eating Engineering 23:38 – RIP to the 2-Pizza Team 26:36 – Paul Graham on Reading as an Advantage 33:31 – The Worst Moves for 2026 37:01 – From Designer to Salesperson Key Points Make the company legible first: keep meeting notes and SOPs in files that an LLM can read. Let the team explore AI tools freely, then ask one department, such as admin, to lead. A million-dollar-a-month app leaves room for niche copies that earn 10K to 30K a month. Build loops that turn daily support transcripts into prototypes and measurable goals. Human support works as a marketing advantage, and premium buyers pay for it. Sales stays part of the owner's job, so learn the frameworks and start cold calling. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND JONATHAN ON SOCIAL Unscheduled CEO Podcast: https://www.unscheduledceo.com/ X/Twitter: https://twitter.com/Jicecream LinkedIn: https://www.linkedin.com/in/jonathan-courtney-4510644b/

I sit down with Billy Howell for an inside look at a real business that runs on Grok Bot agent teams. Billy tests hundreds of tools for The Rundown, and he uses Grok Bot to run his local newsletter, The Arlington Bagel, which goes to 6,000 readers every Thursday. He walks me through his chief of staff bot, his research and sales agents, and the routines that move work forward while he sleeps. We cover the token math, the four-week plan he follows, and the business models that fit this tool best: newsletters, directories, and Shopify stores. You leave with a setup you can copy for one project this month. Timestamps 00:00 – Intro 02:22 – Grok Bot Overview 03:12 – What makes GrokBot special 06:09 – Pick one project 08:09 – Creating your initial agent team 11:19 – First week sprint with Grok Bot 13:06 – Automated Routines within Grok Bot 15:51 – Agent Org Structure 17:35 – Best Practices for building Agents 19:45 – Newsletter playbook 21:12 – Plugins 22:24 – Newsletter playbook pt 2 25:43 – Sales agent 29:06 – Iterating and Improving Agents 33:34 – Grok Bot business ideas 36:45 – Site stack builder 38:36 – Directories and landing pages 41:42 – One project for a month 42:50 – Closing Thoughts Key Points Keep one project per Grok Bot account, so context stays clean and tokens stay available. Start with a chief of staff bot, and let it audit your business and name the first three agents. Follow the four weeks: build the team, execute, hire and fire, then automate. Ask each agent for a five-line brief: what shipped, what is stuck, what needs you. Move repeatable steps into scripts or make com, and keep agent tokens for high value work. Newsletters and directories give the easiest entry, and the two models feed each other. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND BILLY ON SOCIAL: X/Twitter: https://x.com/billyjhowell Instagram: https://www.instagram.com/billyjhowell Tiktok: https://www.tiktok.com/@saasfatigue Youtube: https://www.youtube.com/@billyjhowell

Get Your Complete Financial OS at https://startup-ideas-pod.link/brex_SIP I talk with Remy, known online as AI with Remy, about agent skills and how to share them across a whole team. Remy explains that a skill is an SOP for AI: a markdown file that teaches Claude your exact way to do a task. He then shows his system. He keeps his team skills in one GitHub repository, and he installs that repository as a plugin in Claude Code and Codex. The result is one source of truth, automatic updates for everyone, version control, and company ownership of the work. Listen to this episode if you run daily tasks with agents and you want your whole team to get the same quality of output. Remy’s prompt + skill setup: https://startup-ideas-pod.link/Remy-skills Timestamps 00:00 – Intro 02:33 – Skills as SOPs for AI 04:27 – How the agent uses skills 05:13 – AI is single player 06:21 – Example Skills: Notion, Brand Voice, Email 08:07 – How to share skills 11:40 – What is a Plugin? 14:58 – Anatomy of a Plugin 15:30 – Version Control and Rollback 16:58 – A Second Repo for Personal Skills 18:29 – The Day Claude Deleted 150 Skills 20:02 – Skills as Company Assets 21:00 – A Web App on Top of the Repo 24:46 – How Many Skills to Build 27:00 – The Self-Improvement Loop 28:15 – Skill Maxxing 30:40 – Closing Thoughts Key Points A skill is a markdown SOP that teaches an agent your exact way to do a task. Most skills sit on one laptop, so a great process stays with one person. A GitHub repo plus a plugin gives the team one source of truth for every skill. Auto-update sends each skill edit to every teammate in Claude Code and Codex. A company-owned repo keeps the skills when a teammate moves on. Remy builds thin agents and thick skills, so any harness can run the same process. LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND REMY ON SOCIAL X: https://x.com/remy_gaskell Youtube: https://www.youtube.com/@aiwithremy AI with Remy: https://www.aiwithremy.com/

Get Claude Code: https://startup-ideas-pod.link/claude-greg In this solo episode I lay out the exact system I use to turn Claude Code into what I call an AI employee. My premise is simple: give Claude the same things you would give a person joining your company (a workspace, memory, a brief, a clear ticket, eyes, review, a schedule, and permissions). I build the whole setup live around a real idea I found on ideabrowser.com , a missed-lead responder for med spas, and I share the specific prompts I use at each step. By the end, the product, the customer feedback, the docs, the demos, the reviews, and the recurring work all live inside one operating loop. I close with a seven-day plan you can run at your own pace. And thank you to Claude and Anthropic for supporting the podcast. Setup Claude Code to be your 24/7 employee: https://startup-ideas-pod.link/Claude-code Timestamps 00:00 – Intro: The AI Employee Map 05:47 – Step 1: Creating The Workspace In Claude Desktop 14:53 – Step 2: The Brief And Plan Mode 18:08 – Step 3: The Ticket And Defining Done 22:15 – Step 4: The Eyes And Desktop Preview 26:13 – Step 5: Review In Layers And The Diff View 29:34 – Step 6: The Schedule And Routines 34:32 – Step 7: Parallel Agents And Worktree Isolation 39:14 – Step 8: Permissions: Safe, Ask First, Human-Owned 41:15 – Step 9: Skills, Connectors, And Hooks 44:28 – The Seven-Day Plan 47:42 – Closing Thoughts Key Points I treat Claude Code like a new hire: workspace, memory, brief, ticket, eyes, review, schedule, permissions. The repo brain teaches Claude how I work and what good looks like. Plan mode comes first: Claude reads the context, proposes an approach, and waits for my approval. One ticket at a time means one task, one finish line, and one reviewable change. The eyes matter: Claude opens the app in desktop preview, clicks the flow, checks the console, and reports what a buyer experiences. Routines and permissions turn a chat tool into a 24/7 operator with clear boundaries. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

I sit down with Allie K. Miller to talk about the shift from managing AI agents to enabling them. Allie runs a workforce of 34 AI agents led by an AI chief of staff named Simon, plus six directors named after Friends characters. She shares her three-word prompt, her daily AI diary, her AI watchdogs, and her rule to build the factory before the product. We then debate the future of software: why enterprises still want a vendor to call, and why consumer software now rewards taste and distribution. Listeners leave with one mindset shift and a first step they can finish in under three hours. Get Your Complete Financial OS at https://startup-ideas-pod.link/brex_SIP Timestamps 00:00 – Intro 02:29 – Become a Great Agent Manager 04:49 – The Three-Word Prompt 08:12 – The Pyramid of Proactivity 12:23 – Making the Company Queryable 19:14 – How to Design an AI Workforce 22:25 – AI as a Watchdog 24:56 – Startup Opportunities 26:29 – Build the Factory, Then the Product 30:09 – The SaaS Question 34:53 – Consumer Software as Art 37:12 – High-Value Bottlenecks 44:56 – Closing Thoughts Key Points Allie sits three rungs above her 34 agents. She sets the infrastructure and waits for escalations. Her strongest prompt runs three words on top of full business context: do smart things. She holds the risk tier steady and expands only the breadth and scope of agent work. A dictated daily diary captures the context that lives outside meetings, email, and Slack. AI watchdogs remain wide open: duplicate work, calendar conflicts, and meeting disagreements. The bigger play is a software factory. Build the primitives once, then ship each product faster. I see opportunity on both sides of software: enterprises want a vendor to call, and consumers reward taste. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND ALLIE ON SOCIAL X/Twitter: https://x.com/alliekmiller Instagram: https://www.instagram.com/alliekmiller/ LinkedIn: https://www.linkedin.com/in/alliekmiller/

In this solo episode I break down Cloudflare's AI agent announcement in plain English and explain why I see it as the new business model for the internet. I walk through AI crawl control, pay per crawl, the monetization gateway, and the x402 payment rail, and I show how a single request turns into a transaction. From there I give three startup ideas built directly on top of this shift: a niche data refinery, agent readiness for businesses, and expert archives turned into agent tools. For each idea I lay out the wedge, the first customer, the first version, and how I would sell it. My core claim: the internet is moving from pages humans visit to resources agents use, and the builders who move now own the doors. Timestamps 00:00 – Intro 01:01 – The Old Paradigm of the Internet 02:57 – The New Paradigm of the Internet 03:41 – What Cloudflare is actually doing 06:33 – An AI Index for all our customers 07:34 – The Agent Internet Stack 09:09 – Why Now Is the Best Time to Build 10:29 – Startup Idea 1: The Niche Data Refinery 17:10 – Startup Idea 2: Agent Readiness for Businesses 23:43 – Startup Idea 3: Expert Archives as Agent Tools 30:36 – The Filter for Finding Ideas 32:33 – Closing Thoughts Key Points The human web monetized attention; the agent web monetizes useful resources, priced per request. Cloudflare's pay per crawl, monetization gateway, and x402 turn the HTTP 402 status code into a live checkout at the edge. Idea 1: refine one niche's messy data into clean fuel for agents, starting with 100 businesses in one city. Idea 2: sell agent readiness by showing a founder exactly what AI says about their company today. Idea 3: package an expert's archive into one job-specific agent tool the audience already wants. Every one of these works as a manual services business today and productizes as agent payments mature. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

I bring Cody Schneider back on the show to build two marketing agents end to end, live. The first one monitors LinkedIn posts from creators in your category, scrapes everyone who engages, waterfalls those profiles into emails and phone numbers, and then runs cold email and LinkedIn DMs with an agent managing the replies. The second one turns internal conversations, sales calls, and podcast transcripts into a daily organic LinkedIn content engine across an entire team. Cody names every tool in the stack, shares the real infrastructure costs, and shows the actual terminal commands he runs in Claude Code. By the end you have two systems you can go set up today for your startup. Timestamps 00:00 – Intro 02:27 – Agent Number One: Cold Outbound Agent 04:26 – Finding Creators in Your Category on LinkedIn 09:09 – Apify Explained and the API Maestro Actors 10:59 – Extracting Engagers Live in Claude Code 12:59 – Agent Versus Automation 15:45 – Waterfall Enrichment: GitLeads, Apollo, Origami 17:13 – Compliance, Data Brokers, and What Stays Legal 21:40 – Waterfall Enrichment: Million Verifier and LeadMagic 25:38 – The Cold Outbound Infrastructure 28:33 – Software Factories and Marketing as Code 31:41 – Agent Number Two: The Organic LinkedIn Engine 39:34 – Earned Media Math at $22 CPM 42:31 – Closing Thoughts Key Points LinkedIn engagement is a hand raise, so it beats firmographics as a targeting signal. Ten to twenty source accounts give you roughly 80% surface area coverage of an industry. Waterfall enrichment moves cheapest to most expensive: GitLeads, then Apollo, then Origami or Prospeo. Roughly $200 a month covers sending software plus inboxes for about 10,000 cold emails. An agent here is plain code on a cron job with an LLM attached where judgment is needed. Organic content works best when it starts from real human source material like calls, Slack, and transcripts. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND CODY ON SOCIAL: Cody’s startup: https://www.graphed.com/ X/Twitter: https://x.com/codyschneiderxx Youtube: https://www.youtube.com/@codyschneiderx

I go solo on this one to break down graph engineering, the term I keep seeing go viral on X. I define it in plain English: prompt engineering is how you ask AI a better question, context engineering is how you give AI better information, and graph engineering is how you design the work around the AI so it lives as a managed workflow instead of one giant chat. I walk through the vocabulary (jobs, arrows, state), separate knowledge graphs from agent graphs, and run a full worked example on whether to launch an AI bookkeeping product for Shopify merchants. Then I show three levels of implementation, from manual lanes on a whiteboard up to LangGraph and n8n, plus ready-made graphs for support, content, and code. You leave with a repeatable way to turn one AI workflow you already run into a map of steps, checks, handoffs, loops, and human approvals. Timestamps 00:00 – Intro 01:24 – Prompt Engineering, Context Engineering, Graph Engineering 02:50 – Chat vs Graph 03:35 – Defining Terms and Workflows 06:44 – Knowledge Graphs vs Agent Graphs 08:47 – When to use Graph Engineering 10:01 – Example: AI Bookkeeping For Shopify Merchants 13:22 – The Diamond Pattern Graph Visualized 15:10 – Three Levels of Implementation 17:14 – Customer Support Graph 18:45 – Content Creation Graph 19:30 – Coding Graph 20:42 – The Trap Of Oversized Graphs 22:22 – Building Your First Graph 24:53 – Closing Thoughts Key Points Graph engineering means designing the work around the AI: jobs connected by arrows, with shared state moving between them. Knowledge graphs help AI understand how information connects; agent graphs help AI understand how work should move. Reserve a graph for work with multiple steps, multiple sources, parallel paths, checks, risks, or approvals. Separate the writer from the checker, since a single model grading its own answer inflates confidence. Draw and run the graph manually first; add LangGraph, n8n, or Make com once the structure proves itself. Aim for the smallest graph that raises quality, and place the human gate where mistakes get expensive. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/