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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...

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GREG ISENBERG

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3,270,339 views • 1 day ago •via X (Twitter)

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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 views • 9 days ago

grok bot is literally the closest to a real life Jarvis it can literally start a business, ship software and make you go viral all at once here's how you do it (in 4 mins) 1. install grok bot and make your chief of staff (tony soprano) 2.give it the 'Digital Chief of Staff Prompt' prompt 3.give it access to X, Stripe and Github 4. you are done just paste the article and give it to your chief of staff no matter what it will be the most productive thing you do this week
0:39

grok bot is literally the closest to a real life Jarvis it can literally start a business, ship software and make you go viral all at once here's how you do it (in 4 mins) 1. install grok bot and make your chief of staff (tony soprano) 2.give it the 'Digital Chief of Staff Prompt' prompt 3.give it access to X, Stripe and Github 4. you are done just paste the article and give it to your chief of staff no matter what it will be the most productive thing you do this week

Avid

2,484,826 views • 3 days ago

call me insane... but grok bot is the single most powerful agent stack i've ever used. it will have your entire business run by agent teams working for you 24/7. I just cancelled chatgpt and hermes to replace them with it then mapped the entire grok bot agentic workflows into 4 excalidraw diagrams... (steal this) →support desk: a chief of staff bot routes tickets, drafts replies, pings me only for refunds or anger →money + ops: a bot processes invoices in gmail and keeps the books moving without me →content: a bot scopes the brief, cites sources, hands me a publish-ready draft →build: a bot reproduces the bug, files the ticket, hands the fix to the next bot plus it's all free..give the screenshots to grok bot and it runs your business
0:29

call me insane... but grok bot is the single most powerful agent stack i've ever used. it will have your entire business run by agent teams working for you 24/7. I just cancelled chatgpt and hermes to replace them with it then mapped the entire grok bot agentic workflows into 4 excalidraw diagrams... (steal this) →support desk: a chief of staff bot routes tickets, drafts replies, pings me only for refunds or anger →money + ops: a bot processes invoices in gmail and keeps the books moving without me →content: a bot scopes the brief, cites sources, hands me a publish-ready draft →build: a bot reproduces the bug, files the ticket, hands the fix to the next bot plus it's all free..give the screenshots to grok bot and it runs your business

Avid

18,811 views • 4 days ago

i merged all my AI agents into one brain Claude Code, Codex, Gemini: one team, one memory, zero re-briefing result: 8+ hours a week back, and every agent already knows my projects, my clients and how i think here's the setup with Jarvix: 1. dump your context once: projects, clients, writing style, how you make decisions.. it builds your personal model from your actual AI interactions 2. switch agents mid-task with zero re-briefing: Claude Code builds, Codex reviews, Gemini researches, all reading the same context 3. it carries what YOU choose across sessions.. never start a chat from zero again 4. when it's time to act, it pulls the right agents together as one team instead of five separate assistants the unlock isn't another agent. it's that your context finally travels agents without shared memory are five interns on their first day, forever. agents with it are a team that's worked with you for years not more agents. one shared context that's the right bet P.S. thanks to the Jarvix team for the early access
2:15

i merged all my AI agents into one brain Claude Code, Codex, Gemini: one team, one memory, zero re-briefing result: 8+ hours a week back, and every agent already knows my projects, my clients and how i think here's the setup with Jarvix: 1. dump your context once: projects, clients, writing style, how you make decisions.. it builds your personal model from your actual AI interactions 2. switch agents mid-task with zero re-briefing: Claude Code builds, Codex reviews, Gemini researches, all reading the same context 3. it carries what YOU choose across sessions.. never start a chat from zero again 4. when it's time to act, it pulls the right agents together as one team instead of five separate assistants the unlock isn't another agent. it's that your context finally travels agents without shared memory are five interns on their first day, forever. agents with it are a team that's worked with you for years not more agents. one shared context that's the right bet P.S. thanks to the Jarvix team for the early access

Ronin

14,717 views • 10 days ago

One of the Grok Bot builders: "For the first time I allocated 90% of my work to agents and they did it end to end." In a 30-minute podcast he showed exactly how he uses Grok Bot and how to build an AI agent team from scratch. This beats any paid course on building agents I've seen. Watch it, then read the step-by-step guide on building your AI team for free, with ready-to-copy prompts.
31:10

One of the Grok Bot builders: "For the first time I allocated 90% of my work to agents and they did it end to end." In a 30-minute podcast he showed exactly how he uses Grok Bot and how to build an AI agent team from scratch. This beats any paid course on building agents I've seen. Watch it, then read the step-by-step guide on building your AI team for free, with ready-to-copy prompts.

Anatoli Kopadze

15,823 views • 2 days ago

SpaceXAI just released free 1-hour course on Grok Bot Agents: 1 prompt → CEO bot → agent teams → full 24/7 system from 0% to 100%: 0% → 2:15 - build your first Grok Bot 25% → 6:52 - give every Bot a role 50% → 16:51 - teach Bots to work 24/7 75% → 31:50 - run agent teams in parallel 100% → 52:18 - full system that automate your life most agent tutorials give you a chatbot that waits for prompts - this one builds a 24/7 team that works while your laptop is closed - by team who build it watch it, build one Bot, turn it into a team - then read the full Grok Bot architecture below ↓
1:02:12

SpaceXAI just released free 1-hour course on Grok Bot Agents: 1 prompt → CEO bot → agent teams → full 24/7 system from 0% to 100%: 0% → 2:15 - build your first Grok Bot 25% → 6:52 - give every Bot a role 50% → 16:51 - teach Bots to work 24/7 75% → 31:50 - run agent teams in parallel 100% → 52:18 - full system that automate your life most agent tutorials give you a chatbot that waits for prompts - this one builds a 24/7 team that works while your laptop is closed - by team who build it watch it, build one Bot, turn it into a team - then read the full Grok Bot architecture below ↓

codila

269,577 views • 2 days ago

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch
35:25

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

193,721 views • 4 months ago

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.
4:16

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 views • 1 month ago

I have been testing Grok Bot for the last week and it's EXCELLENT It is your own personal fleet of AI agents doing work for you 24/7 In this video I give you a FULL walkthrough of the tool, how it works, and how to 100x your productivity with it SpaceXAI COOKED
12:09

I have been testing Grok Bot for the last week and it's EXCELLENT It is your own personal fleet of AI agents doing work for you 24/7 In this video I give you a FULL walkthrough of the tool, how it works, and how to 100x your productivity with it SpaceXAI COOKED

Alex Finn

133,092 views • 11 days ago

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

554,775 views • 25 days ago

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 views • 1 month ago

karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch
24:21

karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

GREG ISENBERG

436,752 views • 5 months ago

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 views • 2 months ago

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.
37:47

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

131,267 views • 26 days ago

We’ve been in private beta with a human + AI workspace similar to Buzz, with a few really important differences: (We previously built a company backed by Slack that sold to Atlassian, so we've been thinking about this problem for a while) - Agents reply by default unless you specifically flip to “team chat.” This feels 10x better than having to @ mention the agent every time. - It’s all in the cloud, with shared compute spread across a set of VMs. Way smoother onboarding, and yes, you can still connect your claude/codex subscriptions - You don’t create an army of agents. Number of agents is a vanity metric. Instead, you create shared computers, each one is a “space” where you define shared memory, connectors, skills, automations, etc. The Type agent pulls the appropriate context from the space where you have the conversation. - You can connect it to Slack to make it easy for your team to adopt and slowly move over rather than having to rip out Slack day 1. We’re working with a couple dozen forward thinking companies, who have told us “the whole team loves type”, “it’s the most used app on my computer”, and “type gives you superpowers” Here's a demo of how it works. Reply if you want to test it out!
5:09

We’ve been in private beta with a human + AI workspace similar to Buzz, with a few really important differences: (We previously built a company backed by Slack that sold to Atlassian, so we've been thinking about this problem for a while) - Agents reply by default unless you specifically flip to “team chat.” This feels 10x better than having to @ mention the agent every time. - It’s all in the cloud, with shared compute spread across a set of VMs. Way smoother onboarding, and yes, you can still connect your claude/codex subscriptions - You don’t create an army of agents. Number of agents is a vanity metric. Instead, you create shared computers, each one is a “space” where you define shared memory, connectors, skills, automations, etc. The Type agent pulls the appropriate context from the space where you have the conversation. - You can connect it to Slack to make it easy for your team to adopt and slowly move over rather than having to rip out Slack day 1. We’re working with a couple dozen forward thinking companies, who have told us “the whole team loves type”, “it’s the most used app on my computer”, and “type gives you superpowers” Here's a demo of how it works. Reply if you want to test it out!

Fletcher Richman

370,780 views • 26 days ago

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
58:55

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 views • 5 months ago

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent
2:29

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent

Leonard Rodman

94,961 views • 1 month ago

Grok Bot just killed every overhyped AI setup on your timeline Stop building 40 single-purpose agents that burn your time. Here is the exact system architecture to run a multi-million dollar operation using a managed Grok Bot hosted on a cloud VPS (powered by Cursor and SpaceX infrastructure): The Chief of Staff Architecture: Deploy one central "Chief of Staff" agent authenticated to your email and drive. Route execution down to specialized sub-agents (Dev, Support, YouTube) only when needed. Zero-Code Skill Training: Click record, complete a task on your screen, like triaging emails or reviewing analytics - and GrokBot auto-generates a permanent execution skill from the recording. Unrestricted Cloud Execution: The agent runs 24/7 inside its own Linux container for $200/month, executing tasks asynchronously using MCP servers and Composio to query live data from ChartMogul, Help Scout, and Sentry. Anti-Lock-In Memory Dump: Avoid getting trapped on a single platform. Force your Grok Bot to write daily logs and append context summaries directly to external Google Drive or Notion docs. One single Grok Bot harness with structured sub-agents crushes an army of disconnected, unorganized bots every single time.
11:28

Grok Bot just killed every overhyped AI setup on your timeline Stop building 40 single-purpose agents that burn your time. Here is the exact system architecture to run a multi-million dollar operation using a managed Grok Bot hosted on a cloud VPS (powered by Cursor and SpaceX infrastructure): The Chief of Staff Architecture: Deploy one central "Chief of Staff" agent authenticated to your email and drive. Route execution down to specialized sub-agents (Dev, Support, YouTube) only when needed. Zero-Code Skill Training: Click record, complete a task on your screen, like triaging emails or reviewing analytics - and GrokBot auto-generates a permanent execution skill from the recording. Unrestricted Cloud Execution: The agent runs 24/7 inside its own Linux container for $200/month, executing tasks asynchronously using MCP servers and Composio to query live data from ChartMogul, Help Scout, and Sentry. Anti-Lock-In Memory Dump: Avoid getting trapped on a single platform. Force your Grok Bot to write daily logs and append context summaries directly to external Google Drive or Notion docs. One single Grok Bot harness with structured sub-agents crushes an army of disconnected, unorganized bots every single time.

Shadow Nick

16,706 views • 2 days ago

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.
1:33

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

324,338 views • 4 months ago

Notion 3.0 with Agents is out today! It's the first Knowledge Work Agent in the world. It works with Notion databases. It can do multi-step actions and autonomous work up to 20+ mins, and a brand-new memory system (using Notion pages and databases! 👌) With 3.0, Notion isn’t just your tool — it’s your teammate that works while you sleep. Two types of agents: Personal Agent: Can do anything you can do in Notion. Remembers and learns your preferences as you work. Has a little personality (accessories like a duck hat 🦆.) Custom Agents (early access): Spin up a small team. Hand them the repetitive stuff. They work while you sleep! And Notion Agents work across Notion Calendar, Notion Mail, plus MCP and more. Available today! Try it. Tell us what’s missing. We’ll keep shipping, so you can focus on your life’s work 🫡
2:03

Notion 3.0 with Agents is out today! It's the first Knowledge Work Agent in the world. It works with Notion databases. It can do multi-step actions and autonomous work up to 20+ mins, and a brand-new memory system (using Notion pages and databases! 👌) With 3.0, Notion isn’t just your tool — it’s your teammate that works while you sleep. Two types of agents: Personal Agent: Can do anything you can do in Notion. Remembers and learns your preferences as you work. Has a little personality (accessories like a duck hat 🦆.) Custom Agents (early access): Spin up a small team. Hand them the repetitive stuff. They work while you sleep! And Notion Agents work across Notion Calendar, Notion Mail, plus MCP and more. Available today! Try it. Tell us what’s missing. We’ll keep shipping, so you can focus on your life’s work 🫡

Ivan Zhao

201,218 views • 11 months ago