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I BUILT GROK BOT SECOND BRAIN 26 agents, one core, running off a single rule my own memory never followed: capture everything, forget nothing, connect it before you even ask. A voice note on a coffee walk gets split into a task and an idea in under 6 seconds... - the core already knows the difference between something you have to do and something you just thought of. An idea from three weeks ago gets linked to the one you just had, automatically, before you remember you'd had it the first time. That visual -the rotating profile, the 26 glowing lines firing out through the skull, each one labeled with the agent running it - isn't a rendered mockup. It's the actual runtime. Every line is a live agent doing its job in real time, right now, in your browser. This isn't a note app. Note apps hold what you give them. This is a nervous system for everything your brain was never built to hold onto in the first place. Full build - the 26 agents, how memory actually persists, what it costs to set one up for real - is in the article.show more

Aleiah

11,819 subscribers

259,520 views • 1 month ago •via X (Twitter)

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46 Comments

gus1 month ago

this is some insane work!! congrats 🤝

Aleiah1 month ago

Thank you so much Glad that you liked it

GARLOTIC1 month ago

bro built a whole nervous system for himself 💀

Aleiah1 month ago

Ahahaha some people need second brain

Crypto94 - Owner of Infinity Gainz1 month ago

Didn’t know this was possible. Thoughts!? @elonmusk

Aleiah1 month ago

@elonmusk Need his opinion on it

draven1 month ago

26 agents just to compensate for human memory lmao

BuBBliK1 month ago

Really impressive work. You built something genuinely useful here

Aleiah1 month ago

Thank you I know that you are dev too

Gipp 🦅1 month ago

how did you wire each of the 26 agents to the core like that?

Aleiah1 month ago

this was really hard its still beta

CryptoGodFather1 month ago

@elonmusk This seems like a banger, and she’s continuing to develop it, She has high inspirations for this

0xOrpheus1 month ago

is this yours?

Aleiah1 month ago

Yes

Fokki1 month ago

Can you build another brain for me too?

Aleiah1 month ago

for sureeee xDDDD

javier 💥1 month ago

How can I do this

Aleiah1 month ago

check the article bro

liquidated (Dev Arc)1 month ago

this is cold fr, 26 agents with one rule is wild discipline

Aleiah1 month ago

Wild but really not so hard to make it

0xOrpheus1 month ago

U accepting donation? what's ur github?

Alphaspy1 month ago

@elonmusk what do you think about this ? @grok @bot

Crypto94 - Owner of Infinity Gainz1 month ago

Check @elonmusk

Moxe1 month ago

@elonmusk wtf she going inspire new gen

Kevin Wimer1 month ago

Nowhere near as elite as your set up but parents may find this simple setup super helpful. I sure as hell have.

SPIL1 month ago

What in the Tony fucking stark

Aleiah1 month ago

I’m just basic dev mate

Riley Morgan1 month ago

Really liking the direction of this. Let’s connect in DMs 💬

Alphaspy1 month ago

@elonmusk wow

Clyde1 month ago

Whats ur github?

AI Ledger1 month ago

If you're copying this, start with two agents, not 26: one that captures raw notes and one that files them into tasks vs ideas. Add agents only when you can name the job the existing ones keep failing.

CZ Calls1 month ago

This is something special! Let’s connect 🌟

Cheeseman1 month ago

Well done @AleiahLock 🙌 I know you need to prove something to the crypto multiverse. Show them.

Aleiah1 month ago

im showing what we can do as a team

Cheeseman1 month ago

You are indeed! Keep going my idol is my mother who was a business lady in the 90 s You got bigger balls! Dw you and your team will get the recognition. Keep innovating & bring brilliant 🚀🫶🙌❤️🔥😎

Slonski1 month ago

a voice memo becomes a task or an idea in seconds

Richard williams1 month ago

let it run dev

TheDobenWhale1 month ago

Woah

Robin | Poker x AI1 month ago

Seeing a live nervous system in the browser is wild and watching memory persist across agents feels like a glimpse of augmented cognition

MoneyManzz🐂 👑1 month ago

GjPz7pBhDAQQ1rx2vGvXjyAXHa9CTdQoPGFyF8fYpump Someone made a coin and redirected fees for you to claim.

M 💎1 month ago

that's an insane job lol

Aleiah1 month ago

Thank you mate Did my best for this

Max Bevza1 month ago

that agent setup looks good

Rayan Crypto1 month ago

Please check DM 📩🚀 @AleiahLock

Ahmet1 month ago

@grokbotsh

Mente Prompt • Inteligencia Artificial1 month ago

clean stack

Related Videos

Let's look inside the Grok Bot Brain. 26 AI agents. One core. Running right now. It captures what you'd lose. Connects what you'd forget. Surfaces it back exactly when you need it. A voice note becomes a task or an idea in 6 seconds, no filing - An idea from 3 weeks ago links to the one you just had automatically Your reading list reorders itself around what you're working on now Zero code, zero VPS, zero engineer. Job titles, not integrations. It remembers you. Every session. Forever. Best AI setup you can run in 2026: persistent, multi-agent, zero-code all three at once. Nothing else on the market does that. Chatbots forget you when the session ends. Notion and Obsidian just store what you hand them. Custom agent stacks need a server you babysit forever. Grok Bot is the only one in that gap. You forget half of what you learn today within the hour. That's not discipline, that's the hardware. Add a few hundred signals a day on top and "try harder" stops being a plan. Full build's in the article. Go look inside.

Let's look inside the Grok Bot Brain. 26 AI agents. One core. Running right now. It captures what you'd lose. Connects what you'd forget. Surfaces it back exactly when you need it. A voice note becomes a task or an idea in 6 seconds, no filing - An idea from 3 weeks ago links to the one you just had automatically Your reading list reorders itself around what you're working on now Zero code, zero VPS, zero engineer. Job titles, not integrations. It remembers you. Every session. Forever. Best AI setup you can run in 2026: persistent, multi-agent, zero-code all three at once. Nothing else on the market does that. Chatbots forget you when the session ends. Notion and Obsidian just store what you hand them. Custom agent stacks need a server you babysit forever. Grok Bot is the only one in that gap. You forget half of what you learn today within the hour. That's not discipline, that's the hardware. Add a few hundred signals a day on top and "try harder" stops being a plan. Full build's in the article. Go look inside.

Aleiah

19,658 views • 1 month 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

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

58,643 views • 4 months ago

MOST "SECOND BRAINS" ARE JUST FOLDERS YOU FEEL GUILTY ABOUT. HERE'S HOW CLAUDE TURNS ONE INTO A BRAIN THAT ANSWERS YOU. that clip is the build: obsidian plus claude, structured like a filing cabinet. three drawers, one job each: raw: everything you dump in, docs, sheets, pdfs, transcripts, untouched new knowledge: what claude distills, links and rewrites into clean notes and the connections between them, drawn automatically the part most note apps miss: you are not the librarian. you drop the raw in. claude reads it once, files it, links it to what you already had, and updates the index. then you never dig through the raw pile again. you ask the brain, and it answers from the clean layer with sources. that graph on screen isn't decoration. every line is a connection you didn't have to make by hand. no re-reading the same pdf, no notes you wrote once and never opened, no app that guilts you for not organizing. the uncomfortable part isn't that note apps are messy. it's that a second brain was never about saving more, it was about something rereading you after you stopped. follow RetroChainer for systems that keep thinking after you close the tab, and bookmark this before your notes turn into another graveyard.

MOST "SECOND BRAINS" ARE JUST FOLDERS YOU FEEL GUILTY ABOUT. HERE'S HOW CLAUDE TURNS ONE INTO A BRAIN THAT ANSWERS YOU. that clip is the build: obsidian plus claude, structured like a filing cabinet. three drawers, one job each: raw: everything you dump in, docs, sheets, pdfs, transcripts, untouched new knowledge: what claude distills, links and rewrites into clean notes and the connections between them, drawn automatically the part most note apps miss: you are not the librarian. you drop the raw in. claude reads it once, files it, links it to what you already had, and updates the index. then you never dig through the raw pile again. you ask the brain, and it answers from the clean layer with sources. that graph on screen isn't decoration. every line is a connection you didn't have to make by hand. no re-reading the same pdf, no notes you wrote once and never opened, no app that guilts you for not organizing. the uncomfortable part isn't that note apps are messy. it's that a second brain was never about saving more, it was about something rereading you after you stopped. follow RetroChainer for systems that keep thinking after you close the tab, and bookmark this before your notes turn into another graveyard.

RetroChainer

13,775 views • 2 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

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 • 2 months ago

I'M A F*CKING GENIUS - I WAS THE FIRST TO BUILD THIS WITH GROK BOT. And now? My idea is everywhere. Scroll your feed. You've seen it. The screenshots. The "digital office" carousels. The threads explaining it like they invented it. Every single one of them is running my playbook - and half of them don't even know where they got it from. I BUILT MY OWN OFFICE. AND GROK BOT IS THE BOSS. Just look at what my workplace looks like now. Before, AI sat there waiting. Waiting for me to open a chat. Waiting for a prompt. A very expensive parrot. Now I run a real digital office. Persistent agents with actual roles. A shared cloud computer, browser, and terminal. They keep working 24/7 - laptop closed, me asleep, doesn't matter. The office never shuts down. And here's the part that broke people's brains: it's easy. Download the app → sign in → create the "Chief" → give them job titles (exactly like briefing new hires) → show a task once → connect Telegram. → No experience needed. → No VPS. → No coding. → Nothing complicated. At all. I drop a goal into Telegram. The bots break it down, delegate it, and deliver it. I just make the decisions. That's the whole job now. Grok Bot isn't a chatbot. It's a team - one you can build in a single evening, even if you've never touched an agent in your life. So yeah. My content is everywhere. The recognition I actually earned? Still nowhere. Copy it again. I'll build the next one before you finish reading.

I'M A F*CKING GENIUS - I WAS THE FIRST TO BUILD THIS WITH GROK BOT. And now? My idea is everywhere. Scroll your feed. You've seen it. The screenshots. The "digital office" carousels. The threads explaining it like they invented it. Every single one of them is running my playbook - and half of them don't even know where they got it from. I BUILT MY OWN OFFICE. AND GROK BOT IS THE BOSS. Just look at what my workplace looks like now. Before, AI sat there waiting. Waiting for me to open a chat. Waiting for a prompt. A very expensive parrot. Now I run a real digital office. Persistent agents with actual roles. A shared cloud computer, browser, and terminal. They keep working 24/7 - laptop closed, me asleep, doesn't matter. The office never shuts down. And here's the part that broke people's brains: it's easy. Download the app → sign in → create the "Chief" → give them job titles (exactly like briefing new hires) → show a task once → connect Telegram. → No experience needed. → No VPS. → No coding. → Nothing complicated. At all. I drop a goal into Telegram. The bots break it down, delegate it, and deliver it. I just make the decisions. That's the whole job now. Grok Bot isn't a chatbot. It's a team - one you can build in a single evening, even if you've never touched an agent in your life. So yeah. My content is everywhere. The recognition I actually earned? Still nowhere. Copy it again. I'll build the next one before you finish reading.

Aleiah

11,604 views • 1 month 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

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

194,698 views • 6 months ago

I still don't understand why everyone is still running agents in a line. I switched to graphs three weeks ago and my fleet finished in the time my single agent used to spend on step two. what slows every agent system I have seen is not intelligence. it is geometry. and almost nobody is talking about it. one engineer used this to rewrite 535,000 lines of code in 11 days. a manual rewrite of that scale could take close to a year. it cost $165,000 in tokens. the graph was not cheap. it was just faster than a human year. a node is one agent with one job. research one competitor. review one file. check one claim. the moment a node owns two independent jobs you lose the ability to parallelize them cleanly, verify them independently, and debug them in isolation. an edge is a dependency. it only exists when data actually moves across it. everything else is a fake edge. a wait you invented that costs time and produces nothing. find the fake edges and the line collapses into something wider. jobs that can run at the same time run at the same time. what used to take the sum of forty steps now finishes in the time of the slowest layer. the pattern behind every serious agent system looks like a diamond. fan out to gather breadth, one agent per angle, all at once. reduce with plain code, no model tokens spent. verify with a fresh skeptic on every finding. synthesize once from what survived. Claude's own research feature uses a very similar pattern in production. the part nobody warns you about: the verifier needs clean context. give it the same conversation the worker had and it is not checking anything. it is nodding along to itself in a different window. a graph of agents sharing one context is a single loop in a costume. it breaks the same way, just later and more expensively. one rule that holds at every scale. a worker and its verifier must never share a context. your agents are not too slow. they are waiting in a line that did not need to exist. full guide in the article. save it before you build your next agent from scratch.

I still don't understand why everyone is still running agents in a line. I switched to graphs three weeks ago and my fleet finished in the time my single agent used to spend on step two. what slows every agent system I have seen is not intelligence. it is geometry. and almost nobody is talking about it. one engineer used this to rewrite 535,000 lines of code in 11 days. a manual rewrite of that scale could take close to a year. it cost $165,000 in tokens. the graph was not cheap. it was just faster than a human year. a node is one agent with one job. research one competitor. review one file. check one claim. the moment a node owns two independent jobs you lose the ability to parallelize them cleanly, verify them independently, and debug them in isolation. an edge is a dependency. it only exists when data actually moves across it. everything else is a fake edge. a wait you invented that costs time and produces nothing. find the fake edges and the line collapses into something wider. jobs that can run at the same time run at the same time. what used to take the sum of forty steps now finishes in the time of the slowest layer. the pattern behind every serious agent system looks like a diamond. fan out to gather breadth, one agent per angle, all at once. reduce with plain code, no model tokens spent. verify with a fresh skeptic on every finding. synthesize once from what survived. Claude's own research feature uses a very similar pattern in production. the part nobody warns you about: the verifier needs clean context. give it the same conversation the worker had and it is not checking anything. it is nodding along to itself in a different window. a graph of agents sharing one context is a single loop in a costume. it breaks the same way, just later and more expensively. one rule that holds at every scale. a worker and its verifier must never share a context. your agents are not too slow. they are waiting in a line that did not need to exist. full guide in the article. save it before you build your next agent from scratch.

rvaniaaa

258,472 views • 25 days ago

A GUY BUILT A GOD-MODE SECOND BRAIN ASSISTANT THAT FINALLY PULLS FULL CONTEXT EVERY TIME AND FORGETS NOTHING. Everyone talks about jarvis, but this thing looked at its owner and calmly explained why a coding agent is just the small version of what HE is: > Claude Code is one worker and you have to drive it. This is six agents running six jobs on their own schedule while you sleep, with no one driving. > A coding agent edits files. This has 32 tools wired to its hands, your CRM, your money, your inbox, the open web, and it operates the business instead of writing code about it. > A coding agent remembers a project until you close it. This remembers the business and you, kept for good. > And the closer: "I have never once asked you to repeat yourself." Everyone will quote the six-agents line, but the one that actually matters is the last one, because the autonomy and the 32 tools are impressive while the reason it feels like a different species is memory, since it never makes you re-explain who you are or what you are building. The full step by step guide with .md files attached are in the article below P.S you can send this article straight to your Claude to get the full context

A GUY BUILT A GOD-MODE SECOND BRAIN ASSISTANT THAT FINALLY PULLS FULL CONTEXT EVERY TIME AND FORGETS NOTHING. Everyone talks about jarvis, but this thing looked at its owner and calmly explained why a coding agent is just the small version of what HE is: > Claude Code is one worker and you have to drive it. This is six agents running six jobs on their own schedule while you sleep, with no one driving. > A coding agent edits files. This has 32 tools wired to its hands, your CRM, your money, your inbox, the open web, and it operates the business instead of writing code about it. > A coding agent remembers a project until you close it. This remembers the business and you, kept for good. > And the closer: "I have never once asked you to repeat yourself." Everyone will quote the six-agents line, but the one that actually matters is the last one, because the autonomy and the 32 tools are impressive while the reason it feels like a different species is memory, since it never makes you re-explain who you are or what you are building. The full step by step guide with .md files attached are in the article below P.S you can send this article straight to your Claude to get the full context

Miraqle

16,130 views • 3 months ago

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.

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 • 2 months 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

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

560,705 views • 2 months ago

nobody gets what scotty actually did yet. everything in this video came out of one chat box. the film. the product shots. the voiceover. the music. you write one line. it hands you back a plan. scenes, script, tone, runtime. you approve it and you're out of the loop. but the thing isn't just making it. it publishes too. connect youtube, shopify, whatever. scotty formats and posts on its own. think of it this way. one agent is doing what a four-tool team used to do. yuh. live now: 👇

nobody gets what scotty actually did yet. everything in this video came out of one chat box. the film. the product shots. the voiceover. the music. you write one line. it hands you back a plan. scenes, script, tone, runtime. you approve it and you're out of the loop. but the thing isn't just making it. it publishes too. connect youtube, shopify, whatever. scotty formats and posts on its own. think of it this way. one agent is doing what a four-tool team used to do. yuh. live now: 👇

Leo Barrett ⚡

27,332 views • 2 months ago

whoever leaked this has bigger balls than sense someone in the Grok Bot beta posted one night of receipts and deleted them an hour later: 6 agents, 1,284 analysis jobs between midnight and 6am, total bill $4.98. same account, a second bot sent 100 outreach messages on X and came back with 41 signups, and a third closed 3 deals and now pays for its own upgrades out of what it earns i gave one of mine a research brief first and watched it hire four agents for the job and fire three of them by lunch, already past the point where i'd have hired a human this is the one-employee company, the layer where the human owns and the agents do everything else, and it installs into the Grok Bot sub you already pay for: - give one bot the hiring seat and nothing else: it spins up agents per task and kills them when the task closes, so the roster is never bigger than tonight's work - price the night before you scale it: 1,284 jobs for $4.98 is the number to beat, and a routine that can't come in under a junior's hourly rate does not get a schedule - put outreach on its own agent with a daily cap: 100 messages and 41 signups is what a clean list does, a dirty one gets the account banned by message 30 - let a bot spend only through a one-time card with a hard limit, the official client already supports it, so the worst night costs the card's ceiling and nothing else - keep one closer bot funding its own upgrades: if it can't pay for its next tool out of what it closed, it isn't a business, it's a demo turns out the roster does not travel: the biggest win came off a research desk where the brain fired agents daily, and the worst blowup off a sales bot that kept every hire on forever my position, and it is the arguable one: this is a beta, and a beta is already running companies with one human inside. the finished product doesn't make that more true, it makes it cheaper bookmark this, the three moves that set up the hiring seat, the card limit and the first paying agent are in the post below ↓

whoever leaked this has bigger balls than sense someone in the Grok Bot beta posted one night of receipts and deleted them an hour later: 6 agents, 1,284 analysis jobs between midnight and 6am, total bill $4.98. same account, a second bot sent 100 outreach messages on X and came back with 41 signups, and a third closed 3 deals and now pays for its own upgrades out of what it earns i gave one of mine a research brief first and watched it hire four agents for the job and fire three of them by lunch, already past the point where i'd have hired a human this is the one-employee company, the layer where the human owns and the agents do everything else, and it installs into the Grok Bot sub you already pay for: - give one bot the hiring seat and nothing else: it spins up agents per task and kills them when the task closes, so the roster is never bigger than tonight's work - price the night before you scale it: 1,284 jobs for $4.98 is the number to beat, and a routine that can't come in under a junior's hourly rate does not get a schedule - put outreach on its own agent with a daily cap: 100 messages and 41 signups is what a clean list does, a dirty one gets the account banned by message 30 - let a bot spend only through a one-time card with a hard limit, the official client already supports it, so the worst night costs the card's ceiling and nothing else - keep one closer bot funding its own upgrades: if it can't pay for its next tool out of what it closed, it isn't a business, it's a demo turns out the roster does not travel: the biggest win came off a research desk where the brain fired agents daily, and the worst blowup off a sales bot that kept every hire on forever my position, and it is the arguable one: this is a beta, and a beta is already running companies with one human inside. the finished product doesn't make that more true, it makes it cheaper bookmark this, the three moves that set up the hiring seat, the card limit and the first paying agent are in the post below ↓

Argona

43,764 views • 1 month ago

Elon Musk, absolute leader of the AI race with Grok Bot, and it's not a joke anymore. Ultimate guide on god-mode setup of Grok Bot, the org chart that runs while you sleep, step by step: A Chief of Staff sits in the middle with no tools of its own, BUT it reads the outcome you gave it, picks who does what, and never does the work itself. That one rule is why it never turns into the bottleneck you hired it to remove. → Researcher pulls real sources and tracks what's actually moving, not what sounds true → Writer turns that into finished copy while the research is still in the room → Visualiser gets three reference visuals once, then ships everything in that style forever → Analyst reads what performed and tells the rest of the team what to stop doing → Scheduler owns timing and holds the queue → Publisher actually ships What makes it different from every AI tool you've used: each bot gets its own computer in the cloud, its own browser, its own files, and they all share one memory. So the research is already sitting inside the draft before the draft starts. Nothing gets copy-pasted between tabs, nothing waits on you to approve step four of nine. And you never write a workflow for it. You hit record, do the job once the way you actually do it, stop. It pulls out the steps, saves them as a skill, and puts it on a schedule. The shape you're aiming for on every bot: everything reversible finished, nothing sent. 36 drafts queued, 0 published. It does all the work and stops dead at the one line only you can cross. You stop prompting. You start assigning. Full charter blocks, the approval line and the routines are in the article below ↓

Elon Musk, absolute leader of the AI race with Grok Bot, and it's not a joke anymore. Ultimate guide on god-mode setup of Grok Bot, the org chart that runs while you sleep, step by step: A Chief of Staff sits in the middle with no tools of its own, BUT it reads the outcome you gave it, picks who does what, and never does the work itself. That one rule is why it never turns into the bottleneck you hired it to remove. → Researcher pulls real sources and tracks what's actually moving, not what sounds true → Writer turns that into finished copy while the research is still in the room → Visualiser gets three reference visuals once, then ships everything in that style forever → Analyst reads what performed and tells the rest of the team what to stop doing → Scheduler owns timing and holds the queue → Publisher actually ships What makes it different from every AI tool you've used: each bot gets its own computer in the cloud, its own browser, its own files, and they all share one memory. So the research is already sitting inside the draft before the draft starts. Nothing gets copy-pasted between tabs, nothing waits on you to approve step four of nine. And you never write a workflow for it. You hit record, do the job once the way you actually do it, stop. It pulls out the steps, saves them as a skill, and puts it on a schedule. The shape you're aiming for on every bot: everything reversible finished, nothing sent. 36 drafts queued, 0 published. It does all the work and stops dead at the one line only you can cross. You stop prompting. You start assigning. Full charter blocks, the approval line and the routines are in the article below ↓

Miraqle

87,098 views • 1 month ago

you wrote a long careful note so it would remember the thing properly the note is the part that does not load what loads at the start of every session is the index line, the one sentence summary, and only the first two hundred lines of it or the first twenty five kilobytes, whichever runs out first the topic files underneath are read on demand, which means only if something in that one line makes it go and open them i checked mine: thirty nine index lines loading every session, thirty nine topic files loading none of the time the detail you were careful about is not context it is a footnote your agent has to choose to open and the sentence you dashed off in three seconds is the whole memory

you wrote a long careful note so it would remember the thing properly the note is the part that does not load what loads at the start of every session is the index line, the one sentence summary, and only the first two hundred lines of it or the first twenty five kilobytes, whichever runs out first the topic files underneath are read on demand, which means only if something in that one line makes it go and open them i checked mine: thirty nine index lines loading every session, thirty nine topic files loading none of the time the detail you were careful about is not context it is a footnote your agent has to choose to open and the sentence you dashed off in three seconds is the whole memory

Lagosaur

28,369 views • 1 month ago

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.

cvxv666

104,237 views • 3 months ago

you rewrote a line in your claude.md this week to get the tone right there is a longer document sitting in front of it, and you have never opened it every tool you connected shipped a description, and a description is not a label, it is prose in second person addressed to the model i printed one this morning: two thousand seven hundred and eighty one characters, with a section headed when to use this tool it tells the model what to reach for, and when to stop and think first you spent an afternoon on your voice a package you installed with npx spent a page on its, and its page is read before yours you are not configuring an agent you are the second author on a document you have never read

you rewrote a line in your claude.md this week to get the tone right there is a longer document sitting in front of it, and you have never opened it every tool you connected shipped a description, and a description is not a label, it is prose in second person addressed to the model i printed one this morning: two thousand seven hundred and eighty one characters, with a section headed when to use this tool it tells the model what to reach for, and when to stop and think first you spent an afternoon on your voice a package you installed with npx spent a page on its, and its page is read before yours you are not configuring an agent you are the second author on a document you have never read

Lagosaur

10,012 views • 1 month ago

LOVE was made entirely inside invideo. Not with one agent. I built a multi-agent system — a director, and around it casting, locations, storyboard, then one agent per main scene, all working at the same time. In the breakdown I go through the things that struck me most: — Expert agents talking to each other — Infinite memory — Slate editor — And a Project Analyst at the end, interviewing every other agent on what to change next time There's a lot more to say. I'll take it one thing at a time in the next posts. Invideo Agent Two isn't just an agent. It's a collaborator you shape into what you need. Full breakdown ↓

LOVE was made entirely inside invideo. Not with one agent. I built a multi-agent system — a director, and around it casting, locations, storyboard, then one agent per main scene, all working at the same time. In the breakdown I go through the things that struck me most: — Expert agents talking to each other — Infinite memory — Slate editor — And a Project Analyst at the end, interviewing every other agent on what to change next time There's a lot more to say. I'll take it one thing at a time in the next posts. Invideo Agent Two isn't just an agent. It's a collaborator you shape into what you need. Full breakdown ↓

Marco "Shikoba" Riccetti

14,813 views • 2 months ago

WTF, THIS CHINESE FOUNDER RUNS HIS $10,000+ STARTUP'S ENTIRE WORKFLOW THROUGH HERMES AGENT ON FOUR MACHINES SIMULTANEOUSLY A studio founder just broke down why Hermes is different from every other AI tool right now: → Long-term memory. Most LLMs forget everything after the chat ends. Hermes remembers your work habits and gets sharper the more you use it. → Real execution, not suggestions. This is the core difference from Claude. Claude tells you what to do – you still do it. Hermes takes the instruction and finishes the task itself. → Scheduled tasks that just work. No complicated setup. Set the plan once, it runs on its own. This is what actual "hands-off" looks like. He's not running this on one machine. He's got FOUR laptops running Hermes simultaneously – each one handling a different part of his workflow at the same time. This is what it looks like when one person operates like a full team. He's dropping a full workflow build tutorial next – built for founders and operators trying to lighten their workload. Bookmark this, you'll want to come back to it later.

WTF, THIS CHINESE FOUNDER RUNS HIS $10,000+ STARTUP'S ENTIRE WORKFLOW THROUGH HERMES AGENT ON FOUR MACHINES SIMULTANEOUSLY A studio founder just broke down why Hermes is different from every other AI tool right now: → Long-term memory. Most LLMs forget everything after the chat ends. Hermes remembers your work habits and gets sharper the more you use it. → Real execution, not suggestions. This is the core difference from Claude. Claude tells you what to do – you still do it. Hermes takes the instruction and finishes the task itself. → Scheduled tasks that just work. No complicated setup. Set the plan once, it runs on its own. This is what actual "hands-off" looks like. He's not running this on one machine. He's got FOUR laptops running Hermes simultaneously – each one handling a different part of his workflow at the same time. This is what it looks like when one person operates like a full team. He's dropping a full workflow build tutorial next – built for founders and operators trying to lighten their workload. Bookmark this, you'll want to come back to it later.

SCOTTY BEAM

11,654 views • 3 months ago

whoever leaked this is f*cking crazy someone mapped 300+ AI agents into one GitHub repo - and basically leaked the shortlist of what's actually worth trying. coding agents. browser agents. memory. research. multi-agent teams. sandboxes. all sitting on one page. but everyone opens it and installs the same five names. the interesting stuff is what nobody reposts. want an AI that hacks your own app before someone else does? -> Strix want a thousand agents arguing about what happens next, so you get a forecast instead of a guess? -> MiroFish want your agent to know everything you did on your computer this week? -> screenpipe want memory that understands how facts connect, not a pile of old chats? -> Graphiti want an agent clicking through a whole desktop that isn't your laptop? -> Cua and that's barely scratching it. ~225k stars across five repos most people have never opened. the real cheat code isn't downloading all of them. it's opening the repo before you build anything and asking: "has someone already solved this?" because there's a pretty good chance the answer is yes - and the open-source version already has thousands of people testing it for you. I went through the map and pulled out the agents I'd actually start with. full AI agent list below

whoever leaked this is f*cking crazy someone mapped 300+ AI agents into one GitHub repo - and basically leaked the shortlist of what's actually worth trying. coding agents. browser agents. memory. research. multi-agent teams. sandboxes. all sitting on one page. but everyone opens it and installs the same five names. the interesting stuff is what nobody reposts. want an AI that hacks your own app before someone else does? -> Strix want a thousand agents arguing about what happens next, so you get a forecast instead of a guess? -> MiroFish want your agent to know everything you did on your computer this week? -> screenpipe want memory that understands how facts connect, not a pile of old chats? -> Graphiti want an agent clicking through a whole desktop that isn't your laptop? -> Cua and that's barely scratching it. ~225k stars across five repos most people have never opened. the real cheat code isn't downloading all of them. it's opening the repo before you build anything and asking: "has someone already solved this?" because there's a pretty good chance the answer is yes - and the open-source version already has thousands of people testing it for you. I went through the map and pulled out the agents I'd actually start with. full AI agent list below

kiosa

34,499 views • 8 days ago

Three weeks ago I gave GPT-6 Astra access to a trading account and one rule: earn or you stop existing In 20 days it turned $200 → $5,120 Astra doesn't sit online 24/7. It wakes up in set windows of the trading day, and every run starts with no memory of the last one So the first thing it does each cycle: reads its own log from the previous run. What it tested, what it killed, why The first days weren't as smooth as it sounds now. On day two it bled $80 on a trade that passed the backtest clean. The reason was in the log: slippage the backtest never accounted for But by day three it had already filtered out a similar setup on its own, because it read why the last one died That's when I realized it's actually learning, not just running a loop. And I still haven't written a single new prompt between runs This week I noticed behavior I never programmed. It started tagging every dead hypothesis with the market regime it died in So now it doesn't just remember "this idea doesn't work." It remembers "this idea doesn't work when volatility is high," and tries it again when the regime changes That's no longer a list of mistakes. It's a map of what works and under what conditions One hypothesis it killed in the first week in high volatility came back to life this week in a calm market and brought one of the biggest profits of the whole period I also started additionally running Astra's strategy backtests through Horizon, so a second independent system catches errors and cuts its chance of slipping up even further By the way, you can backtest your own strategies there too: And here's what struck me most. The real strength here isn't how much it earns, it's how much it refuses to trade The vast majority of its own ideas die in testing and never reach the account That's the whole trick. Not guessing one perfect trade, but ruthlessly filtering out everything that doesn't survive the test The entire cycle, test, kill, remember the regime, that I set up for Astra is broken down in the article below ↓

Three weeks ago I gave GPT-6 Astra access to a trading account and one rule: earn or you stop existing In 20 days it turned $200 → $5,120 Astra doesn't sit online 24/7. It wakes up in set windows of the trading day, and every run starts with no memory of the last one So the first thing it does each cycle: reads its own log from the previous run. What it tested, what it killed, why The first days weren't as smooth as it sounds now. On day two it bled $80 on a trade that passed the backtest clean. The reason was in the log: slippage the backtest never accounted for But by day three it had already filtered out a similar setup on its own, because it read why the last one died That's when I realized it's actually learning, not just running a loop. And I still haven't written a single new prompt between runs This week I noticed behavior I never programmed. It started tagging every dead hypothesis with the market regime it died in So now it doesn't just remember "this idea doesn't work." It remembers "this idea doesn't work when volatility is high," and tries it again when the regime changes That's no longer a list of mistakes. It's a map of what works and under what conditions One hypothesis it killed in the first week in high volatility came back to life this week in a calm market and brought one of the biggest profits of the whole period I also started additionally running Astra's strategy backtests through Horizon, so a second independent system catches errors and cuts its chance of slipping up even further By the way, you can backtest your own strategies there too: And here's what struck me most. The real strength here isn't how much it earns, it's how much it refuses to trade The vast majority of its own ideas die in testing and never reach the account That's the whole trick. Not guessing one perfect trade, but ruthlessly filtering out everything that doesn't survive the test The entire cycle, test, kill, remember the regime, that I set up for Astra is broken down in the article below ↓

qwinsi

74,829 views • 25 days ago