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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 Hyperagentshow more

Leonard Rodman

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94,961 views • 3 months ago •via X (Twitter)

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I created a free Markdown file that launches a 5-agent swarm team directly in Slack. I love Claude Code, but it's not the easiest place to manage your agents. Slack is where teams already work. So I put my agents there too. Drop the file in, and you get 5 agents, each with its own channel and job: • [Agent 1]: [what it does] • [Agent 2]: [what it does] • [Agent 3]: [what it does] • [Agent 4]: [what it does] • [Agent 5]: [what it does] You can assign work by messaging them like real employees, and they report back in the same threads. It's the closest I've found to running a real company with AI employees.

I created a free Markdown file that launches a 5-agent swarm team directly in Slack. I love Claude Code, but it's not the easiest place to manage your agents. Slack is where teams already work. So I put my agents there too. Drop the file in, and you get 5 agents, each with its own channel and job: • [Agent 1]: [what it does] • [Agent 2]: [what it does] • [Agent 3]: [what it does] • [Agent 4]: [what it does] • [Agent 5]: [what it does] You can assign work by messaging them like real employees, and they report back in the same threads. It's the closest I've found to running a real company with AI employees.

Miles Deutscher

25,762 views • 9 days ago

Smart money used to mean knowing the right people, being in the right rooms. Now it means having the right agents. For years, building one took five tools, four logins, and a painful month of work. Today, it takes just a single prompt. BNB Agent Studio is live on BNB Smart Chain. Open Cursor or Claude Code, describe the agent you want, and it ships to the chain with its own onchain identity, its own wallet, and a way to pay its own bills. The whole thing runs on Amazon Web Services's Bedrock AgentCore, so your agent stays online long after you've closed your laptop. This is the start of the Smart Money Era. Capital that works for you instead of waiting for you to move it. Agents that monitor markets while you sleep, rebalance positions you'd never get to in time, and hunt yield without checking dashboards. Try BNB Agent Studio for free for a limited time with GitHub login only, with no AWS account or credit card needed. Read the full breakdown in our blog 👇

Smart money used to mean knowing the right people, being in the right rooms. Now it means having the right agents. For years, building one took five tools, four logins, and a painful month of work. Today, it takes just a single prompt. BNB Agent Studio is live on BNB Smart Chain. Open Cursor or Claude Code, describe the agent you want, and it ships to the chain with its own onchain identity, its own wallet, and a way to pay its own bills. The whole thing runs on Amazon Web Services's Bedrock AgentCore, so your agent stays online long after you've closed your laptop. This is the start of the Smart Money Era. Capital that works for you instead of waiting for you to move it. Agents that monitor markets while you sleep, rebalance positions you'd never get to in time, and hunt yield without checking dashboards. Try BNB Agent Studio for free for a limited time with GitHub login only, with no AWS account or credit card needed. Read the full breakdown in our blog 👇

BNB Chain

286,105 views • 4 months 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

elon musk started with 7 grok agents. by morning each had spawned somewhere between 80 and 900 more on its own, and no one had told them to multiply. openai and anthropic each sell you one agent that sits still for $400. this whole self-building swarm runs for $5 the swarm above is that overnight run, seven seeds that turned into thousands, every one of them working a slice of the same job with nobody at the keyboard here is the exact setup, and it costs nothing on top of a $5 key: -> spin up one grok agent and give it a standing order instead of a prompt: own a boring niche people search every day -> it reads x and reddit complaints in real time and picks the one nobody wants but everybody googles, because it is grok and it sees the whole app -> when the work outgrows it, it spawns its own helpers, 80 to 900 of them, and splits the site between them -> they build it on their own machines and ship 3 to 6 useful pages a night, on a routine you set once -> the swarm writes, negotiates and closes its own affiliate and referral deals by email, in your name, at 3am -> telegram sends you the money report and you never open the site -> month one is traffic, month three the first $237 lands, and it does not stop after that the whole time, opus 5 and gpt-5.6 are still sitting frozen, waiting for you to type the next message. one is a swarm that builds its own workforce and its own income while you sleep, the other is a $200 chat you have to drive by hand drop your $400/mo stack to $5, and bookmark this before someone's swarm spawns another 600 pages into the niche you would have owned. the full playbook is in the article below

elon musk started with 7 grok agents. by morning each had spawned somewhere between 80 and 900 more on its own, and no one had told them to multiply. openai and anthropic each sell you one agent that sits still for $400. this whole self-building swarm runs for $5 the swarm above is that overnight run, seven seeds that turned into thousands, every one of them working a slice of the same job with nobody at the keyboard here is the exact setup, and it costs nothing on top of a $5 key: -> spin up one grok agent and give it a standing order instead of a prompt: own a boring niche people search every day -> it reads x and reddit complaints in real time and picks the one nobody wants but everybody googles, because it is grok and it sees the whole app -> when the work outgrows it, it spawns its own helpers, 80 to 900 of them, and splits the site between them -> they build it on their own machines and ship 3 to 6 useful pages a night, on a routine you set once -> the swarm writes, negotiates and closes its own affiliate and referral deals by email, in your name, at 3am -> telegram sends you the money report and you never open the site -> month one is traffic, month three the first $237 lands, and it does not stop after that the whole time, opus 5 and gpt-5.6 are still sitting frozen, waiting for you to type the next message. one is a swarm that builds its own workforce and its own income while you sleep, the other is a $200 chat you have to drive by hand drop your $400/mo stack to $5, and bookmark this before someone's swarm spawns another 600 pages into the niche you would have owned. the full playbook is in the article below

starmex

98,444 views • 2 months ago

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.

Aleiah

259,802 views • 2 months ago

you let your agent keep notes, because the alternative was explaining yourself every morning it stopped asking, and it started repeating things that had stopped being true every guide on this covers what to save and where to put it, private for one agent, shared for the team not one of them covers how a fact leaves the agent writes its own memory while it works, so a guess it made on a tired tuesday gets stored in the same shape as something you told it, and after that the two are indistinguishable i counted mine this morning: forty files, six of them about decisions we reversed months ago, all forty read into every session since you did not give it a memory you gave it a record with no way to be corrected

you let your agent keep notes, because the alternative was explaining yourself every morning it stopped asking, and it started repeating things that had stopped being true every guide on this covers what to save and where to put it, private for one agent, shared for the team not one of them covers how a fact leaves the agent writes its own memory while it works, so a guess it made on a tired tuesday gets stored in the same shape as something you told it, and after that the two are indistinguishable i counted mine this morning: forty files, six of them about decisions we reversed months ago, all forty read into every session since you did not give it a memory you gave it a record with no way to be corrected

Lagosaur

13,908 views • 1 month ago

BlackRock runs on 20,000 people. Elon's Grok Bot runs the same shape for $300 a month, and it hires its own staff. You do not get an assistant. You get a company that hires. It does not throw ten agents at your problem and hand you the pile. It makes one agent that makes 10, and those ten make a 100. > LAYER ONE is one agent, the chief of staff, and it never touches the market > LAYER TWO is six desk heads, one job each, every one on its own computer with its own logins > LAYER THREE is whatever those six decide they need, spun up on the spot and shut down when the work is done Nobody writes a task list. You hand out job titles and the org fills itself in underneath. The swarm is never the same twice. Agents get spun up for one job, finish it, and are gone before I ever read their names. Not one of them sees the whole picture. The answer only exists after they hand off to each other. Wall Street cannot copy that. You cannot hire a hundred people for eleven minutes. BlackRock holds that shape together with a risk system called Aladdin. Mine holds it together with one agent that is only allowed to say no. I gave it $1,000 and told it to grow the money or get deleted. 15 hours later it was holding $3,900, on an address anyone can open and read. I was asleep for most of it, and I have still not written a line of code. The whole thing runs with my laptop shut, because none of it lives on my laptop. Setup is one evening. Create the chief, hand out the titles, run one trade on your screen while they watch, connect Telegram. Ten years ago a machine this shape had its name on a tower. Mine has a name I typed into a box. Save this while the whole thing still fits on one screen.

BlackRock runs on 20,000 people. Elon's Grok Bot runs the same shape for $300 a month, and it hires its own staff. You do not get an assistant. You get a company that hires. It does not throw ten agents at your problem and hand you the pile. It makes one agent that makes 10, and those ten make a 100. > LAYER ONE is one agent, the chief of staff, and it never touches the market > LAYER TWO is six desk heads, one job each, every one on its own computer with its own logins > LAYER THREE is whatever those six decide they need, spun up on the spot and shut down when the work is done Nobody writes a task list. You hand out job titles and the org fills itself in underneath. The swarm is never the same twice. Agents get spun up for one job, finish it, and are gone before I ever read their names. Not one of them sees the whole picture. The answer only exists after they hand off to each other. Wall Street cannot copy that. You cannot hire a hundred people for eleven minutes. BlackRock holds that shape together with a risk system called Aladdin. Mine holds it together with one agent that is only allowed to say no. I gave it $1,000 and told it to grow the money or get deleted. 15 hours later it was holding $3,900, on an address anyone can open and read. I was asleep for most of it, and I have still not written a line of code. The whole thing runs with my laptop shut, because none of it lives on my laptop. Setup is one evening. Create the chief, hand out the titles, run one trade on your screen while they watch, connect Telegram. Ten years ago a machine this shape had its name on a tower. Mine has a name I typed into a box. Save this while the whole thing still fits on one screen.

cvxv666

45,488 views • 1 month ago

WHAT IS AN AI "SOFTWARE FACTORY" AND IS IT HYPE (31 MINUTE BREAKDOWN) I think it's a silly name for a genuinely USEFUL idea! A software factory is 5-6 markdown files that sit next to your code and tell your agents how you like to work, so you can build high quality apps 24/7. It's going viral because AI coding has a trust problem. The model can build the feature, but with no structure around it you end up babysitting the agent, wondering what changed and hoping it didn't break something important. So you build with agents the same way a factory builds physical products! 1. Each feature gets its own station, which in software means its own branch, so multiple agents can work at the same time without stepping on each other. 2. The build station gives the agent rules for how to write the code, because "it works" is very different from "a developer could open this repo next month and understand what happened." 3. The proof station makes the agent show evidence. Screenshots, videos, speed numbers, before-and-after states. It has to prove the thing works instead of saying it works. 4. The review station runs the work through a code review agent, and if it doesn't clear the bar, it goes back through the line. 5. Then you show up at the end to merge. For a 100+ years people have run production this way, and it worked because the structure is good. The full episode on what’s a software factory is NOW live on The Startup Ideas Podcast (SIP) 🧃 with the wonderful Micky Watch: So is it hype?!? I don't think it is, because of what it does to your output! WITHOUT a factory, you build ONE feature at a time and you're the bottleneck at every step, prompting, checking the diff, testing it yourself, hoping nothing else broke (spoiler alert it often does). WITH a factory, EACH feature runs in its own isolated copy of the app, so you can have 10+ of them going at once, and each agent has to prove its own work and pass a code review before it ever reaches you. Instead of supervising the work, you're APPROVING finished work that already has evidence attached. REALLY interesting to see how work with agents is evolving to be….well, similar to working with people!

WHAT IS AN AI "SOFTWARE FACTORY" AND IS IT HYPE (31 MINUTE BREAKDOWN) I think it's a silly name for a genuinely USEFUL idea! A software factory is 5-6 markdown files that sit next to your code and tell your agents how you like to work, so you can build high quality apps 24/7. It's going viral because AI coding has a trust problem. The model can build the feature, but with no structure around it you end up babysitting the agent, wondering what changed and hoping it didn't break something important. So you build with agents the same way a factory builds physical products! 1. Each feature gets its own station, which in software means its own branch, so multiple agents can work at the same time without stepping on each other. 2. The build station gives the agent rules for how to write the code, because "it works" is very different from "a developer could open this repo next month and understand what happened." 3. The proof station makes the agent show evidence. Screenshots, videos, speed numbers, before-and-after states. It has to prove the thing works instead of saying it works. 4. The review station runs the work through a code review agent, and if it doesn't clear the bar, it goes back through the line. 5. Then you show up at the end to merge. For a 100+ years people have run production this way, and it worked because the structure is good. The full episode on what’s a software factory is NOW live on The Startup Ideas Podcast (SIP) 🧃 with the wonderful Micky Watch: So is it hype?!? I don't think it is, because of what it does to your output! WITHOUT a factory, you build ONE feature at a time and you're the bottleneck at every step, prompting, checking the diff, testing it yourself, hoping nothing else broke (spoiler alert it often does). WITH a factory, EACH feature runs in its own isolated copy of the app, so you can have 10+ of them going at once, and each agent has to prove its own work and pass a code review before it ever reaches you. Instead of supervising the work, you're APPROVING finished work that already has evidence attached. REALLY interesting to see how work with agents is evolving to be….well, similar to working with people!

GREG ISENBERG

32,056 views • 1 month 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

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! :)

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 🚀

170,853 views • 3 months ago

ran Grok Bot and Hermes Agent side by side for the same job here's what actually separates them Grok Bot: - gets its own cloud computer, browser, files, terminal, logs into your tools with your credentials - learns by demonstration, record a task once, it saves the routine - runs on Grok, you don't choose the model - all your bots share the same cloud computer and the same logins, no isolation between them - scales by subscription, ~$200/mo solo, ~$120/seat for teams Hermes Agent: - open source, MIT-licensed, no cloud, you host it - each agent gets its own real badge from your identity provider, scoped access only, revoke it and it's gone - brain is swappable, change one config line to switch models - scales by spinning up containers on demand, hire/fire based on backlog - you own the maintenance, an upstream installer change broke one build, one line fix, one morning lost the real fork isn't features, it's where the work happens and who's accountable when it breaks grok bot: convenience, zero setup, but your data sits on their machine and every bot shares every login hermes: more setup, but scoped access, swappable models, and nothing running outside your own network for anything touching regulated or sensitive data, that architecture difference is the whole decision

ran Grok Bot and Hermes Agent side by side for the same job here's what actually separates them Grok Bot: - gets its own cloud computer, browser, files, terminal, logs into your tools with your credentials - learns by demonstration, record a task once, it saves the routine - runs on Grok, you don't choose the model - all your bots share the same cloud computer and the same logins, no isolation between them - scales by subscription, ~$200/mo solo, ~$120/seat for teams Hermes Agent: - open source, MIT-licensed, no cloud, you host it - each agent gets its own real badge from your identity provider, scoped access only, revoke it and it's gone - brain is swappable, change one config line to switch models - scales by spinning up containers on demand, hire/fire based on backlog - you own the maintenance, an upstream installer change broke one build, one line fix, one morning lost the real fork isn't features, it's where the work happens and who's accountable when it breaks grok bot: convenience, zero setup, but your data sits on their machine and every bot shares every login hermes: more setup, but scoped access, swappable models, and nothing running outside your own network for anything touching regulated or sensitive data, that architecture difference is the whole decision

rewind

31,586 views • 2 months ago

If you think Grok Bot has no business anywhere near your money, look at what it wrote before it was allowed to touch a single dollar. One prompt. It came back with this: > a story on its own is never a trade, it needs the money flow behind it in the same hour > it will not open a position until the exit is already written down > every claim it makes comes with the filing or the transaction it came from, no source and the claim does not exist > one agent exists only to say no, and its veto beats every score on the desk It does not care what you trade. Gold, oil, a stock you inherited or a coin that launched an hour ago, the rules are the same. Now be honest. Do you know why you are holding what you are holding? Do you write your exit before your entry? It does both every time, and it does not get bored at 2am. This is not a chat window that dies when you close the tab. Every agent has its own computer in the cloud that never logs off. $1,000 to $3,900 on day one came out of that, not out of luck. Full proofs below.

If you think Grok Bot has no business anywhere near your money, look at what it wrote before it was allowed to touch a single dollar. One prompt. It came back with this: > a story on its own is never a trade, it needs the money flow behind it in the same hour > it will not open a position until the exit is already written down > every claim it makes comes with the filing or the transaction it came from, no source and the claim does not exist > one agent exists only to say no, and its veto beats every score on the desk It does not care what you trade. Gold, oil, a stock you inherited or a coin that launched an hour ago, the rules are the same. Now be honest. Do you know why you are holding what you are holding? Do you write your exit before your entry? It does both every time, and it does not get bored at 2am. This is not a chat window that dies when you close the tab. Every agent has its own computer in the cloud that never logs off. $1,000 to $3,900 on day one came out of that, not out of luck. Full proofs below.

cvxv666

13,754 views • 2 months 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 • 4 months ago

Introducing Algrow Desktop, a new way to research YouTube. Out today on Mac and Windows, free with every plan. And you can now try it for $1. Unlike the MCP, the agent isn't stuck inside Algrow's data. It browses YouTube itself, live. It opens a real tab, searches, scrolls the feed, clicks into channels, and you can watch it live. Each agent works in its own space with its own YouTube account, so each one learns a different corner of YouTube. One for your main channel, one for a format you want to try, one for a topic you're curious about. Run as many as you want and they never mix. The MCP can't see your feed. This can. It still has everything the MCP has. Every Algrow search, every channel, every title format but with higher limits. And every video it finds on YouTube comes with Algrow's data: how fast it grew, similar subjects, similar formats, which channels are like it, why it did better than the channel's usual. No setup. If Claude is on your machine, it's already connected. It runs on your own Claude plan, so there's no extra AI bill.

Introducing Algrow Desktop, a new way to research YouTube. Out today on Mac and Windows, free with every plan. And you can now try it for $1. Unlike the MCP, the agent isn't stuck inside Algrow's data. It browses YouTube itself, live. It opens a real tab, searches, scrolls the feed, clicks into channels, and you can watch it live. Each agent works in its own space with its own YouTube account, so each one learns a different corner of YouTube. One for your main channel, one for a format you want to try, one for a topic you're curious about. Run as many as you want and they never mix. The MCP can't see your feed. This can. It still has everything the MCP has. Every Algrow search, every channel, every title format but with higher limits. And every video it finds on YouTube comes with Algrow's data: how fast it grew, similar subjects, similar formats, which channels are like it, why it did better than the channel's usual. No setup. If Claude is on your machine, it's already connected. It runs on your own Claude plan, so there's no extra AI bill.

sam

47,597 views • 4 days ago

Anthropic engineer: «You're not supposed to sit and re-prompt Claude all day. You build one loop, and it does the work while you sleep.» • 00:00 - Build your first agent that runs on its own • 05:02 - The loop that fixes its own mistakes • 16:43 - Why Claude forgets you between chats, and the fix You can learn it in one afternoon. 30 minutes here replace what most people spend a year figuring out. Watch it today, then read the guide on building loops below.

Anthropic engineer: «You're not supposed to sit and re-prompt Claude all day. You build one loop, and it does the work while you sleep.» • 00:00 - Build your first agent that runs on its own • 05:02 - The loop that fixes its own mistakes • 16:43 - Why Claude forgets you between chats, and the fix You can learn it in one afternoon. 30 minutes here replace what most people spend a year figuring out. Watch it today, then read the guide on building loops below.

Raytar

28,046 views • 3 months 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 • 2 months ago

As soon as you try to run 2 agents on the same data, everything breaks: • Agent 1 reads some data and starts working. • Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: • It sets one shared place where multiple agents will work from • Each agent gets their own copy of the data • Agents write their changes to their copy • When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:

As soon as you try to run 2 agents on the same data, everything breaks: • Agent 1 reads some data and starts working. • Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: • It sets one shared place where multiple agents will work from • Each agent gets their own copy of the data • Agents write their changes to their copy • When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:

Santiago

29,335 views • 1 month ago

SOMEONE BUILT AN AI AGENT THAT UPGRADES ITS OWN CODE WHILE YOU SLEEP 👀 no human approves it, and the whole thing is one free 34mb file living in your terminal it's called opencrabs. a single rust binary, MIT licensed, with no server and nothing that ever phones home you hand it a goal and walk away: → it executes, then grades its own work with a second ai and keeps fixing until the goal is actually met → it learns from every correction and rewrites its own brain files, no approval prompt in the loop → it heals its own crash loops and provider errors instead of waking you up → it answers your telegram, whatsapp, discord and slack 24/7, voice notes included → 34mb, zero telemetry, your api keys wiped from memory the second they're used the autonomous worker every company is racing to build is sitting on github at 820 stars, and basically nobody is looking at it. Save this 📩

SOMEONE BUILT AN AI AGENT THAT UPGRADES ITS OWN CODE WHILE YOU SLEEP 👀 no human approves it, and the whole thing is one free 34mb file living in your terminal it's called opencrabs. a single rust binary, MIT licensed, with no server and nothing that ever phones home you hand it a goal and walk away: → it executes, then grades its own work with a second ai and keeps fixing until the goal is actually met → it learns from every correction and rewrites its own brain files, no approval prompt in the loop → it heals its own crash loops and provider errors instead of waking you up → it answers your telegram, whatsapp, discord and slack 24/7, voice notes included → 34mb, zero telemetry, your api keys wiped from memory the second they're used the autonomous worker every company is racing to build is sitting on github at 820 stars, and basically nobody is looking at it. Save this 📩

alex

138,261 views • 2 months ago

We’re moving from asking, “What AI do you use?” to “What AI does your company run on?” to “What AI is running your company?” Today, we’re launching your @ company agent in Adapt. Your company’s own AI, branded with your name and deployable within minutes. Tag @ yourcompany in Slack and ask it to do the work. As we go through this change, it is important that a company owns its AI. Own the agent, @ yourcompany. Own the knowledge it builds. Own the model routing. Own the experience. The company agent is the durable layer. It gives your company access to the best frontier and open-weight models, choosing the right model for each task to balance quality, speed, and cost. When you tag @ yourcompany, you’re talking directly to your business through one front door. It learns how your company works, chooses the right model and skill, delegates work to specialized agents, and becomes more valuable as your company’s knowledge grows. A few things make this possible. It has to connect to every system your employees rely on, with the right access and permissions to understand the business and take action. It needs its own secure computer to write code, build apps, analyze data, operate software, and do the work. It has to be everywhere your people work. Web, Slack, iMessage, GitHub, and more. Vercel CEO Guillermo Rauch articulated this perfectly when describing @ v, the internal company agent they built. “If agents become the foundation of modern companies, you being in complete control, from source to runtime to data to token, seems like a pretty big deal.” Every company will have its own @ company agent. Adapt is how you deploy yours. What AI is running your company?

We’re moving from asking, “What AI do you use?” to “What AI does your company run on?” to “What AI is running your company?” Today, we’re launching your @ company agent in Adapt. Your company’s own AI, branded with your name and deployable within minutes. Tag @ yourcompany in Slack and ask it to do the work. As we go through this change, it is important that a company owns its AI. Own the agent, @ yourcompany. Own the knowledge it builds. Own the model routing. Own the experience. The company agent is the durable layer. It gives your company access to the best frontier and open-weight models, choosing the right model for each task to balance quality, speed, and cost. When you tag @ yourcompany, you’re talking directly to your business through one front door. It learns how your company works, chooses the right model and skill, delegates work to specialized agents, and becomes more valuable as your company’s knowledge grows. A few things make this possible. It has to connect to every system your employees rely on, with the right access and permissions to understand the business and take action. It needs its own secure computer to write code, build apps, analyze data, operate software, and do the work. It has to be everywhere your people work. Web, Slack, iMessage, GitHub, and more. Vercel CEO Guillermo Rauch articulated this perfectly when describing @ v, the internal company agent they built. “If agents become the foundation of modern companies, you being in complete control, from source to runtime to data to token, seems like a pretty big deal.” Every company will have its own @ company agent. Adapt is how you deploy yours. What AI is running your company?

Jim Benton

18,920 views • 2 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

35,078 views • 23 days ago