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The "marketing engineer" is the NEW forward deployed engineer, and I think the BEST ones will make $1M a year! A forward deployed engineer embeds with your team and uses AI to build the workflows The marketing engineer does that BUT for growth, they build AI agents that find...

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GREG ISENBERG

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204,054 просмотров • 1 день назад •via X (Twitter)

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The Marketing Engineer is the most important hire a marketing team can make in 2026. Not to run campaigns, but to build the agents that run them. Today we're introducing a new role to the marketing org: 𝗧𝗵𝗲 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 We believe every business on the planet will eventually need one. Their job is to build the Agents, automations, and systems that create a new kind of marketing. Part-builder, part-strategist. They build Agents that monitor media coverage, flag sentiment shifts, and draft journalist outreach before your PR team even knows there's a story. They build Agents that track every competitor move, grade its severity, and rewrite your battle cards and talk tracks before your next sales call. They build Agents that turn every webinar into a brand-voice article, refreshed weekly and queued for your team to review before posting. Our first marketing hire Nick Lafferty has already become 's Founding Marketing Engineer, the first person to hold this title at a company fully behind it. Every marketing team on the planet will use AI to do their jobs better. But to build truly impactful systems, you need an owner and an orchestrator. That's the Marketing Engineer.
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The Marketing Engineer is the most important hire a marketing team can make in 2026. Not to run campaigns, but to build the agents that run them. Today we're introducing a new role to the marketing org: 𝗧𝗵𝗲 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 We believe every business on the planet will eventually need one. Their job is to build the Agents, automations, and systems that create a new kind of marketing. Part-builder, part-strategist. They build Agents that monitor media coverage, flag sentiment shifts, and draft journalist outreach before your PR team even knows there's a story. They build Agents that track every competitor move, grade its severity, and rewrite your battle cards and talk tracks before your next sales call. They build Agents that turn every webinar into a brand-voice article, refreshed weekly and queued for your team to review before posting. Our first marketing hire Nick Lafferty has already become 's Founding Marketing Engineer, the first person to hold this title at a company fully behind it. Every marketing team on the planet will use AI to do their jobs better. But to build truly impactful systems, you need an owner and an orchestrator. That's the Marketing Engineer.

Dylan Babbs

324,544 просмотров • 4 месяцев назад

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch
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What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

GREG ISENBERG

84,718 просмотров • 2 месяцев назад

Marketing agents are the NEW coding agents. It's code in the cloud that makes decisions off your live business data on a loop. It researches, acts, reads the results, improves, and goes again. Picture an agent that runs your entire Facebook ad account by itself. It researches your customer's pain points, generates on-brand creative, publishes it, kills the losers, scales the winners, and makes more of whatever's working. We show you the EXACT stack. Perplexity to scrape Reddit for real pain points, Nano Banana for on-brand static creative, a vision model to check it against brand guidelines, HeyGen for AI UGC video, all wired into a loop that reads Facebook's data and reacts. Now point it at a business. What are some startup ideas you can point marketing agents to? WordPress runs 43% of the internet. Take the plugins people already pay for, like Yoast, WooCommerce, and WP Forms, and build the AI-first version of each. Examples: Yoast (~$15M ARR) shows you red and green dots and tells you to fix your SEO yourself. The AI version just does it. Proven demand, no AI-native competition, and thousands of site owner pay agenices $1,000 a month and many wish they'd pay less and got more. Coding agents changed who gets to build software. Marketing agents change who gets to grow a company. Full breakdown on The Startup Ideas Podcast (SIP) 🧃. Thanks to Cody Schneider for the sauce. Will break down more marketing agents if people are interested? Just LMK. Watch. I'm rooting for you and happy growing. I think we've heard a lot about coding agents recently. And we're about to hear a lot more about marketing agents.
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Marketing agents are the NEW coding agents. It's code in the cloud that makes decisions off your live business data on a loop. It researches, acts, reads the results, improves, and goes again. Picture an agent that runs your entire Facebook ad account by itself. It researches your customer's pain points, generates on-brand creative, publishes it, kills the losers, scales the winners, and makes more of whatever's working. We show you the EXACT stack. Perplexity to scrape Reddit for real pain points, Nano Banana for on-brand static creative, a vision model to check it against brand guidelines, HeyGen for AI UGC video, all wired into a loop that reads Facebook's data and reacts. Now point it at a business. What are some startup ideas you can point marketing agents to? WordPress runs 43% of the internet. Take the plugins people already pay for, like Yoast, WooCommerce, and WP Forms, and build the AI-first version of each. Examples: Yoast (~$15M ARR) shows you red and green dots and tells you to fix your SEO yourself. The AI version just does it. Proven demand, no AI-native competition, and thousands of site owner pay agenices $1,000 a month and many wish they'd pay less and got more. Coding agents changed who gets to build software. Marketing agents change who gets to grow a company. Full breakdown on The Startup Ideas Podcast (SIP) 🧃. Thanks to Cody Schneider for the sauce. Will break down more marketing agents if people are interested? Just LMK. Watch. I'm rooting for you and happy growing. I think we've heard a lot about coding agents recently. And we're about to hear a lot more about marketing agents.

GREG ISENBERG

132,157 просмотров • 1 месяц назад

You can create an AI Agent that answers your email with a few clicks. 1. Go to ChatLLM ( 2. Click on AI Engineer 3. Select Create an AI Agent 4. Choose the Email Answering Agent ChatLLM will do the rest: it will code, test, and deploy the agent for you. You can also create a custom agent in English. The Agent Economy is coming (somebody should write a book and use this title.) We are going to see examples like this, times 1,000 in 2025. Just think about how many repetitive tasks you perform every day. Some of these tasks are involved enough that we couldn't automate them with pre-AI solutions. That's where we'll see agents explode, and I'm here for it.
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You can create an AI Agent that answers your email with a few clicks. 1. Go to ChatLLM ( 2. Click on AI Engineer 3. Select Create an AI Agent 4. Choose the Email Answering Agent ChatLLM will do the rest: it will code, test, and deploy the agent for you. You can also create a custom agent in English. The Agent Economy is coming (somebody should write a book and use this title.) We are going to see examples like this, times 1,000 in 2025. Just think about how many repetitive tasks you perform every day. Some of these tasks are involved enough that we couldn't automate them with pre-AI solutions. That's where we'll see agents explode, and I'm here for it.

Santiago

79,974 просмотров • 1 год назад

Agents are now your customers. Here are 9 startup ideas for that customer: - An agency that makes agents cite you. - A system that gives an agent an identity and permissions. - Receipts and audit trails for the work an agent does. - A generator that makes your documents ready for agents. - Security for the agent that reads your inbox. - A service that makes your pricing page readable to an agent. - MCP servers for franchises. - A support desk that agents call. - A sandbox where agents test your software. Pick one and build it.
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Agents are now your customers. Here are 9 startup ideas for that customer: - An agency that makes agents cite you. - A system that gives an agent an identity and permissions. - Receipts and audit trails for the work an agent does. - A generator that makes your documents ready for agents. - Security for the agent that reads your inbox. - A service that makes your pricing page readable to an agent. - MCP servers for franchises. - A support desk that agents call. - A sandbox where agents test your software. Pick one and build it.

The Startup Ideas Podcast (SIP) 🧃

13,505 просмотров • 4 дней назад

Grok Bot might be the first tool that lets one non-technical person run an entire business with a team of AI agents. My friend Billy runs his whole newsletter business on Grok Bot agents, and I think we're about to see 100,000+ businesses like his. BEST PRACTICES: 1. The agents run on a shared cloud computer, so running your newsletter, your X, and your receipts all in one place creates context bloat and burns tokens fast. One mission per setup. 2. Start with a Chief of Staff. Give it access to your existing docs (Notion, Slack, Gmail), have it audit the business, then tell you the top three agents to build first to drive revenue. 3. Perfect a task with the Chief of Staff before spinning up a new agent. Have it do the outbound sales once, review it, and only then say "now build a bot that does exactly that." You earn each new hire by proving the task works first. 4. Constraints are the feature. You get a limited number of agents, one thread per bot, like DMs with a teammate. It forces you to stay mission-oriented instead of spinning up a bot for every random idea. 5. You make the decisions, not the agent. Billy's team spent three weeks unable to pick where content should live. At some point you say "we're doing Notion, no more tinkering" and move on. 6. Run week one with no new agents. Build the team, learn to fly the plane, just execute. Week three is when you find the real gaps and expand, someone to man the inbox, someone for the Shopify shop. 7. Then add routines so it works while you sleep. Ask your Chief of Staff what recurring jobs would move the business forward overnight, and it builds the automations that run without you. Thanks to Billy Howell for sharing the sauce on The Startup Ideas Podcast (SIP) 🧃 (follow for more). Grokbot is really cool. Watch below:
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Grok Bot might be the first tool that lets one non-technical person run an entire business with a team of AI agents. My friend Billy runs his whole newsletter business on Grok Bot agents, and I think we're about to see 100,000+ businesses like his. BEST PRACTICES: 1. The agents run on a shared cloud computer, so running your newsletter, your X, and your receipts all in one place creates context bloat and burns tokens fast. One mission per setup. 2. Start with a Chief of Staff. Give it access to your existing docs (Notion, Slack, Gmail), have it audit the business, then tell you the top three agents to build first to drive revenue. 3. Perfect a task with the Chief of Staff before spinning up a new agent. Have it do the outbound sales once, review it, and only then say "now build a bot that does exactly that." You earn each new hire by proving the task works first. 4. Constraints are the feature. You get a limited number of agents, one thread per bot, like DMs with a teammate. It forces you to stay mission-oriented instead of spinning up a bot for every random idea. 5. You make the decisions, not the agent. Billy's team spent three weeks unable to pick where content should live. At some point you say "we're doing Notion, no more tinkering" and move on. 6. Run week one with no new agents. Build the team, learn to fly the plane, just execute. Week three is when you find the real gaps and expand, someone to man the inbox, someone for the Shopify shop. 7. Then add routines so it works while you sleep. Ask your Chief of Staff what recurring jobs would move the business forward overnight, and it builds the automations that run without you. Thanks to Billy Howell for sharing the sauce on The Startup Ideas Podcast (SIP) 🧃 (follow for more). Grokbot is really cool. Watch below:

GREG ISENBERG

4,004,406 просмотров • 11 дней назад

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.
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This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,715 просмотров • 2 месяцев назад

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch
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AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

376,293 просмотров • 5 месяцев назад

A conversational agent with no hallucinations! This is huge! Here is the $1M problem: Build a customer-facing AI agent that works consistently, doesn't hallucinate, and is fully aligned with the business rules. Check out Parlant, a FREE, open-source library. I've talked about them before, and here is the TL;DR: 1. Parlant gives you 10x more control over what your agent says 2. It helps you control how agents conduct conversations 3. It helps you control the service protocols and rules agents follow 4. It helps you control what agents can and can't say They do something pretty clever: 1. They have a set of predefined potential responses that the agent can use to reply to the customer. 2. When a customer sends a message, the agent generates a fluid response draft using an LLM and matches and renders the closest pre-approved reply template. 3. The agent returns the rendered pre-approved reply, not the one generated by the LLM. This maintains fluidity while avoiding unpredictability. Parlant also uses your domain terminology, invokes your tools, and implements self-critique to craft/evaluate its response iteratively. This pretty much solves any hallucination issues with your agent.
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A conversational agent with no hallucinations! This is huge! Here is the $1M problem: Build a customer-facing AI agent that works consistently, doesn't hallucinate, and is fully aligned with the business rules. Check out Parlant, a FREE, open-source library. I've talked about them before, and here is the TL;DR: 1. Parlant gives you 10x more control over what your agent says 2. It helps you control how agents conduct conversations 3. It helps you control the service protocols and rules agents follow 4. It helps you control what agents can and can't say They do something pretty clever: 1. They have a set of predefined potential responses that the agent can use to reply to the customer. 2. When a customer sends a message, the agent generates a fluid response draft using an LLM and matches and renders the closest pre-approved reply template. 3. The agent returns the rendered pre-approved reply, not the one generated by the LLM. This maintains fluidity while avoiding unpredictability. Parlant also uses your domain terminology, invokes your tools, and implements self-critique to craft/evaluate its response iteratively. This pretty much solves any hallucination issues with your agent.

Santiago

64,237 просмотров • 1 год назад

ANTHROPIC OPEN-SOURCED A FREE REPO THAT TURNS CLAUDE INTO A SALES REP, A MARKETER AND A FINANCE LEAD. PEOPLE ARE ALREADY SELLING THIS AS A SERVICE You're not prompting from scratch. You're hiring a department How to set it up: 1. Open Cowork. In the Claude desktop app, open Cowork and go to Customize. You'll see a Browse Plugins button. That's the marketplace 2. Pick your first role. Inside you'll find sales, marketing, finance, legal, data, customer support, product management and more. Each plugin has the skills for that role already baked in, plus the connectors it would use. Install the one you need most 3. Put it to work standalone. It works day one with no setup. Trigger a workflow with a slash command like /sales:call-prep or /data:write-query, paste your notes, and it already knows what good output looks like 4. Connect its tools to supercharge it. Authorize the tools that role uses, your CRM, your analytics, your data warehouse. Standalone is the intern. Connected is the senior hire 5. Build the rest of the team. Install more roles and they work together in one session. Data pulls the numbers, finance reconciles them, marketing turns it into a report. One operator, a full cross-functional team, no payroll This is the same base layer Claude for Legal and Claude for Financial Services are built on. You're getting it for free Full breakdown in my article below. Bookmark this
0:30

ANTHROPIC OPEN-SOURCED A FREE REPO THAT TURNS CLAUDE INTO A SALES REP, A MARKETER AND A FINANCE LEAD. PEOPLE ARE ALREADY SELLING THIS AS A SERVICE You're not prompting from scratch. You're hiring a department How to set it up: 1. Open Cowork. In the Claude desktop app, open Cowork and go to Customize. You'll see a Browse Plugins button. That's the marketplace 2. Pick your first role. Inside you'll find sales, marketing, finance, legal, data, customer support, product management and more. Each plugin has the skills for that role already baked in, plus the connectors it would use. Install the one you need most 3. Put it to work standalone. It works day one with no setup. Trigger a workflow with a slash command like /sales:call-prep or /data:write-query, paste your notes, and it already knows what good output looks like 4. Connect its tools to supercharge it. Authorize the tools that role uses, your CRM, your analytics, your data warehouse. Standalone is the intern. Connected is the senior hire 5. Build the rest of the team. Install more roles and they work together in one session. Data pulls the numbers, finance reconciles them, marketing turns it into a report. One operator, a full cross-functional team, no payroll This is the same base layer Claude for Legal and Claude for Financial Services are built on. You're getting it for free Full breakdown in my article below. Bookmark this

Yarchi

31,798 просмотров • 2 месяцев назад

It’s time. The biggest news in our six years history. 🤝 Meet the new Stacked Marketer. Not just a newsletter anymore. But a platform for marketers to evolve and advance professionally. By marketers, for marketers. You won't find flashy lifestyle and status posts here— only useful content that helps you scale your business and career. … All by The Crew you can trust to filter out the noise and drivel the marketing industry is known for. What’s changed? 🔷 Careful attention to user experience both in the inbox and on our website. With countless devices, all with their quirks, we know it’s impossible to be perfect here. But we made a big leap forward, and we have the fundamentals to continue to improve in the future. 🔷 An educational platform to help marketers evolve and thrive in their jobs. Our Stacked Marketer Pro platform got the biggest upgrade of all, with a total revamp to enhance user experience that helps members learn more, faster, and better. 🔷 A bolder and more vibrant design across all our products, with easily identifiable common elements so you know at a glance that they come from The Crew here at Stacked Marketer. A single headquarters that unites: 🟦 Stacked Marketer - for news and insights from Monday to Friday. 🟦 Psychology of Marketing - for psychological insights on customer behavior and marketing techniques. 🟦 Stacked Marketer Pro - for education and advancement 🟦 Tactics - for ready-to-apply marketing wins. … in one accessible place. As usual, all carefully curated or created by The Crew at Stacked Marketer to help you take a step forward in your career, income, and professional life. We’re building the marketers’ headquarters we wished existed when we started out, and the way we want it to exist today for our current needs. Our course library is also growing. Copywriting. Newsletters. Google Ads. Meta Ads. Google Analytics 4. Getting Clients. Pinterest Ads. Email Marketing for E-Commerce. TikTok Ads. And more to come. All accessible with a single membership. This new upgrade to the content and user experience in Stacked Marketer is the next step in that process.
0:55

It’s time. The biggest news in our six years history. 🤝 Meet the new Stacked Marketer. Not just a newsletter anymore. But a platform for marketers to evolve and advance professionally. By marketers, for marketers. You won't find flashy lifestyle and status posts here— only useful content that helps you scale your business and career. … All by The Crew you can trust to filter out the noise and drivel the marketing industry is known for. What’s changed? 🔷 Careful attention to user experience both in the inbox and on our website. With countless devices, all with their quirks, we know it’s impossible to be perfect here. But we made a big leap forward, and we have the fundamentals to continue to improve in the future. 🔷 An educational platform to help marketers evolve and thrive in their jobs. Our Stacked Marketer Pro platform got the biggest upgrade of all, with a total revamp to enhance user experience that helps members learn more, faster, and better. 🔷 A bolder and more vibrant design across all our products, with easily identifiable common elements so you know at a glance that they come from The Crew here at Stacked Marketer. A single headquarters that unites: 🟦 Stacked Marketer - for news and insights from Monday to Friday. 🟦 Psychology of Marketing - for psychological insights on customer behavior and marketing techniques. 🟦 Stacked Marketer Pro - for education and advancement 🟦 Tactics - for ready-to-apply marketing wins. … in one accessible place. As usual, all carefully curated or created by The Crew at Stacked Marketer to help you take a step forward in your career, income, and professional life. We’re building the marketers’ headquarters we wished existed when we started out, and the way we want it to exist today for our current needs. Our course library is also growing. Copywriting. Newsletters. Google Ads. Meta Ads. Google Analytics 4. Getting Clients. Pinterest Ads. Email Marketing for E-Commerce. TikTok Ads. And more to come. All accessible with a single membership. This new upgrade to the content and user experience in Stacked Marketer is the next step in that process.

Stacked Marketer

19,863 просмотров • 2 лет назад

This is insane. An AI agent can run every boring job in outbound. We spent the last 6 months building ours. Here are my 8 favorite agents to build: Replies get sorted before we open the inbox. Campaigns go live from one command. Weak inboxes pull themselves out before they hurt a domain. Here are the agents behind it: 1. Reply Agent Reads every reply and drafts the response. A human reviews, edits, and sends. 2. Mailbox Health Agent Watches inbox and domain health. It predicts when you need new mailboxes, then buys and warms them. 3. Campaign Optimizer Agent Checks every live campaign every 6 hours. If 500+ leads were emailed and replies are under 4%, it tests new copy, replaces inboxes replying under 1%, and shifts sending to better hours. 4. Lead Qualification Agent Scores each lead by company size, industry, and tech stack. It enriches the record, updates your CRM, and only loads qualified leads into campaigns. High-priority prospect? You get a Slack ping. 5. Meeting Booking Agent Finds meeting requests inside replies. It books the slot, writes prep notes using the lead's background, sends reminders, and logs the outcome. 6. Pipeline Progression Agent Tracks opens, clicks, and website visits. It moves the CRM stage, triggers the next sequence, and creates a task when a lead shows real intent. 7. Copywriting Agent Writes cold emails and follow-ups in the campaign's voice. 8. Analytics Agent Watches campaign metrics in real time and explains what to fix in plain language. We built ours with custom code. Smartlead's SmartAgents let you build agents like these from a plain-English prompt, inside the platform where your campaigns already run. If you repeat an outbound task more than twice a week, that is an agent you have not built yet. Which one would you build first?
0:45

This is insane. An AI agent can run every boring job in outbound. We spent the last 6 months building ours. Here are my 8 favorite agents to build: Replies get sorted before we open the inbox. Campaigns go live from one command. Weak inboxes pull themselves out before they hurt a domain. Here are the agents behind it: 1. Reply Agent Reads every reply and drafts the response. A human reviews, edits, and sends. 2. Mailbox Health Agent Watches inbox and domain health. It predicts when you need new mailboxes, then buys and warms them. 3. Campaign Optimizer Agent Checks every live campaign every 6 hours. If 500+ leads were emailed and replies are under 4%, it tests new copy, replaces inboxes replying under 1%, and shifts sending to better hours. 4. Lead Qualification Agent Scores each lead by company size, industry, and tech stack. It enriches the record, updates your CRM, and only loads qualified leads into campaigns. High-priority prospect? You get a Slack ping. 5. Meeting Booking Agent Finds meeting requests inside replies. It books the slot, writes prep notes using the lead's background, sends reminders, and logs the outcome. 6. Pipeline Progression Agent Tracks opens, clicks, and website visits. It moves the CRM stage, triggers the next sequence, and creates a task when a lead shows real intent. 7. Copywriting Agent Writes cold emails and follow-ups in the campaign's voice. 8. Analytics Agent Watches campaign metrics in real time and explains what to fix in plain language. We built ours with custom code. Smartlead's SmartAgents let you build agents like these from a plain-English prompt, inside the platform where your campaigns already run. If you repeat an outbound task more than twice a week, that is an agent you have not built yet. Which one would you build first?

Hosun Chung

156,625 просмотров • 1 месяц назад

Every founder and marketer needs to set up AI workflows in 2026. Not optional anymore. The people who figure this out first are going to have an absurd advantage over everyone else. I recorded a full walkthrough of the setup I use to run most of my marketing. Ads, SEO, emails, community, thumbnails, dashboards. I'll show you how all of it works and how you can set it up yourself.
20:19

Every founder and marketer needs to set up AI workflows in 2026. Not optional anymore. The people who figure this out first are going to have an absurd advantage over everyone else. I recorded a full walkthrough of the setup I use to run most of my marketing. Ads, SEO, emails, community, thumbnails, dashboards. I'll show you how all of it works and how you can set it up yourself.

Gael Breton

13,702 просмотров • 6 месяцев назад

EVERYTHING you NEED to know about Jack Dorsey's AI agent "Slack killer" Buzz (set up, use-cases etc in 38 mins) What we get into: 1. What Buzz actually is and should founders switch from Slack? 2. How to swap the model under any agent and keep all your context? 3. How to talk to your agents live with audio huddles? 4. How to get agents to build and deploy real apps for you, like a full CRM from one ask? 5. How to set up the context loop that feeds your live app data back to your agents? 6. How to share compute so a few people split one machine running a local model? 7. Who it's actually for right now, and what's still rough? Full breakdown on the pod The Startup Ideas Podcast (SIP) 🧃. Thanks to Vinny for jumping on and clearly explaining jack and team's latest product. My TLDR take is Buzz is a glimpse into the future of work. Some of you will roll your eyes at that, and I get it, it's alpha software and it's slow in places. But the core idea, that your context is the foundation and agents build out from there, is right, and that's worth seeing early. Watch Curious what you think
38:43

EVERYTHING you NEED to know about Jack Dorsey's AI agent "Slack killer" Buzz (set up, use-cases etc in 38 mins) What we get into: 1. What Buzz actually is and should founders switch from Slack? 2. How to swap the model under any agent and keep all your context? 3. How to talk to your agents live with audio huddles? 4. How to get agents to build and deploy real apps for you, like a full CRM from one ask? 5. How to set up the context loop that feeds your live app data back to your agents? 6. How to share compute so a few people split one machine running a local model? 7. Who it's actually for right now, and what's still rough? Full breakdown on the pod The Startup Ideas Podcast (SIP) 🧃. Thanks to Vinny for jumping on and clearly explaining jack and team's latest product. My TLDR take is Buzz is a glimpse into the future of work. Some of you will roll your eyes at that, and I get it, it's alpha software and it's slow in places. But the core idea, that your context is the foundation and agents build out from there, is right, and that's worth seeing early. Watch Curious what you think

GREG ISENBERG

556,084 просмотров • 1 месяц назад

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.
8:17

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The Startup Ideas Podcast (SIP) 🧃

13,598 просмотров • 2 месяцев назад

There are 8 billion people on earth. Soon there'll be 100 billion AI agents. Every one of them needs email. Six weeks ago I said the next wave of teams would run email through an agent instead of a dashboard. Today it ships. Nitrosend☄️ is launching Agentic Email Marketing: the email layer for the agent economy. What agents can do on Nitrosend right now: Sign themselves up. Point any agent at and it creates the account, connects your domain, sorts billing and sends its first email. No API key. No dashboard. No human required. Shipped, and users agents signing up with it daily. Get their own inboxes (beta, by request). Real addresses on the domain you own. Your agents receive, and send 1-1 email conversations with customers. A reply lands at 3am, your agent answers it. Anything that needs a human gets escalated to you. Ask us and we'll flick yours on. Next: Agentic Outreach (coming soon). Your agent studies your best customers, finds more like them, writes like a person, sends in sequence and works the replies. Then: set a goal and walk away. Goal-based agentic marketing is in development. "20% more activations this quarter" and Nitrosend plans, sends, measures and improves every week. Why we built this: Gmail is agent hostile and expensive per seat. Legacy email platforms assume a human sitting in a dashboard. agents needed an email layer of their own. They're already better at it than we are. They read everything, never miss a follow-up, and write personally at any scale. *94%* of actions on Nitrosend already happen inside an agent (Claude, Codex, ChatGPT, Cursor), not in our UI. Humans approve. Agents operate. This is our third email company. Six billion emails across the first two. We've been burned by every ugly part of email already, which is why the approval gates are built in exactly where you want them. Watch the launch, then send your agent to work: send it.
1:13

There are 8 billion people on earth. Soon there'll be 100 billion AI agents. Every one of them needs email. Six weeks ago I said the next wave of teams would run email through an agent instead of a dashboard. Today it ships. Nitrosend☄️ is launching Agentic Email Marketing: the email layer for the agent economy. What agents can do on Nitrosend right now: Sign themselves up. Point any agent at and it creates the account, connects your domain, sorts billing and sends its first email. No API key. No dashboard. No human required. Shipped, and users agents signing up with it daily. Get their own inboxes (beta, by request). Real addresses on the domain you own. Your agents receive, and send 1-1 email conversations with customers. A reply lands at 3am, your agent answers it. Anything that needs a human gets escalated to you. Ask us and we'll flick yours on. Next: Agentic Outreach (coming soon). Your agent studies your best customers, finds more like them, writes like a person, sends in sequence and works the replies. Then: set a goal and walk away. Goal-based agentic marketing is in development. "20% more activations this quarter" and Nitrosend plans, sends, measures and improves every week. Why we built this: Gmail is agent hostile and expensive per seat. Legacy email platforms assume a human sitting in a dashboard. agents needed an email layer of their own. They're already better at it than we are. They read everything, never miss a follow-up, and write personally at any scale. *94%* of actions on Nitrosend already happen inside an agent (Claude, Codex, ChatGPT, Cursor), not in our UI. Humans approve. Agents operate. This is our third email company. Six billion emails across the first two. We've been burned by every ugly part of email already, which is why the approval gates are built in exactly where you want them. Watch the launch, then send your agent to work: send it.

George Hartley ☄️

935,209 просмотров • 1 месяц назад

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.
3:38

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,647 просмотров • 1 год назад

How to build a 1-person AI company that: - Runs locally - 100% open-source - No human employees, all agents - Real-time collaboration via email Multi-agent orchestration is not new. Plenty of frameworks already let agents hand off tasks, run in parallel, and talk to each other. So the interesting question is not whether agents can collaborate. It is what structure you use to make them collaborate. The common approach is to wire a graph of nodes and edges and reason about the plumbing yourself. It works, but you are learning a new abstraction just to describe who does what. There is a coordination structure we have trusted for a hundred years already: an organization. Every company runs the same way. People have roles, roles have reporting lines, and work moves up and down that chart without anyone relaying each message by hand. Map that onto agents and the whole thing gets intuitive. You lay out an org chart, each agent fills one role, you talk to the person at the top, and the org sorts out the work between them. You already know how a company works, so you already know how to run one here. There is no new abstraction to learn. That is exactly what Alook does. Each agent is a live Claude Code or OpenCode session with a defined role, a reporting line, and its own email inbox. The agents coordinate over email, the same way a team would. And it all runs locally through a runtime on your own machine, so nothing leaves your setup. You bring your own agent too. Claude Code and Codex both work, and if you would rather stay fully open source and local, OpenCode works the same way. To show how this feels in practice, I set up three agents as a small sales team. Vi is the one I talk to. I hand Vi a goal, and Vi routes the work down the chart. Neile runs prospect research. Vi passes the target criteria, and Neile reports back a ranked list of names, roles, and companies, each with a suggested angle and a confidence score. Lliane runs outreach. Vi hands over the messaging angle and follow-up cadence, and Lliane reports back on emails sent, responses received, and any deal that needs escalation. I never relay a message between them. Neile and Lliane report to Vi, and Vi updates me in one place. The whole thing is open source and self-hosted, so it runs on your machine with your own agents. Give the repo a star if you want to follow where it goes: I also wrote a full walkthrough on building your own AI company with it, from a blank org chart to a running job. The article is quoted below. Cheers! :)
0:39

How to build a 1-person AI company that: - Runs locally - 100% open-source - No human employees, all agents - Real-time collaboration via email Multi-agent orchestration is not new. Plenty of frameworks already let agents hand off tasks, run in parallel, and talk to each other. So the interesting question is not whether agents can collaborate. It is what structure you use to make them collaborate. The common approach is to wire a graph of nodes and edges and reason about the plumbing yourself. It works, but you are learning a new abstraction just to describe who does what. There is a coordination structure we have trusted for a hundred years already: an organization. Every company runs the same way. People have roles, roles have reporting lines, and work moves up and down that chart without anyone relaying each message by hand. Map that onto agents and the whole thing gets intuitive. You lay out an org chart, each agent fills one role, you talk to the person at the top, and the org sorts out the work between them. You already know how a company works, so you already know how to run one here. There is no new abstraction to learn. That is exactly what Alook does. Each agent is a live Claude Code or OpenCode session with a defined role, a reporting line, and its own email inbox. The agents coordinate over email, the same way a team would. And it all runs locally through a runtime on your own machine, so nothing leaves your setup. You bring your own agent too. Claude Code and Codex both work, and if you would rather stay fully open source and local, OpenCode works the same way. To show how this feels in practice, I set up three agents as a small sales team. Vi is the one I talk to. I hand Vi a goal, and Vi routes the work down the chart. Neile runs prospect research. Vi passes the target criteria, and Neile reports back a ranked list of names, roles, and companies, each with a suggested angle and a confidence score. Lliane runs outreach. Vi hands over the messaging angle and follow-up cadence, and Lliane reports back on emails sent, responses received, and any deal that needs escalation. I never relay a message between them. Neile and Lliane report to Vi, and Vi updates me in one place. The whole thing is open source and self-hosted, so it runs on your machine with your own agents. Give the repo a star if you want to follow where it goes: I also wrote a full walkthrough on building your own AI company with it, from a blank org chart to a running job. The article is quoted below. Cheers! :)

Akshay 🚀

169,957 просмотров • 1 месяц назад

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
0:24

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,186 просмотров • 2 месяцев назад

You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.
3:10

You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.

The Startup Ideas Podcast (SIP) 🧃

18,006 просмотров • 19 дней назад