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GROK BOT ARCHITECTURE IS NOW RUNNING the same system Elon reposted has grown into a live agent operation you can follow in real time you can watch the agents move through tasks, exchange information and complete work on the live operations floor six Grok agents work inside one shared... system signal → research → context → build → independent review → final result Helm routes the work, Scout finds signals, Archive collects context, Forge builds, Sentinel reviews and Relay prepares the final output i created GBA myself so i can keep building the system, improve the agents and fund the infrastructure it needs as it grows the public UI code and every visible update will continue here: github/monokernn/Grok_Bot_Architectureshow more

monokern

4,782 subscribers

15,787 次观看 • 20 天前 •via X (Twitter)

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17 条评论

brizzy 的头像

brizzy20 天前

broke faggot

Kahuna 的头像

Kahuna20 天前

ts littt 0xe2f888673ab2467146e33f079fdb7be09285d5da

Xav 的头像

Xav20 天前

Just need more eyes

PamanDegen 的头像

PamanDegen20 天前

Farm on sol then to hood Ass wide..

padafnu 的头像

padafnu20 天前

yo my guy, i bought the moment you tweeted the ca, unfortuantly i did some work on the one on sol and got left with some peanuts but it is what it is, im in the one at rh now and hope i can make some bag back here

AXIALIS | Desktop App Tools 的头像

AXIALIS | Desktop App Tools20 天前

Does Sentinel get a clean context window, or can it see Forge’s reasoning and intermediate state?

brizzy 的头像

brizzy20 天前

someone actually drop a nuke on this brokies house

Martin 的头像

Martin20 天前

oh, grats for creating first project

Pips.skr 的头像

Pips.skr20 天前

Lets see this , dev still build this project $GBA @elonmusk

Webby💧(drool/arc) 的头像

Webby💧(drool/arc)20 天前

what does holding the token give you?

KlyraA 的头像

KlyraA20 天前

agents walking the floor and updating shared state live is still rare to see done this clean.

Perk🥀 的头像

Perk🥀20 天前

Watching six grok agents work together in real time on a live board is actually super cool

Kaplan 的头像

Kaplan20 天前

dex paid bro

Dekos 的头像

Dekos20 天前

Overall, it's a cool topic if you look into it.

Logics 的头像

Logics20 天前

Believing in ur building Keep going bro

Flower Eye Store 的头像

Flower Eye Store20 天前

The live operations floor is so cool to watch. Love the Sentinel review step!

kocer 的头像

kocer20 天前

six grok agents working live on one floor is actually interesting to watch

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GROK BOT solana:AP8Wnu37Gf9RHgugPKGvpHe6LcTE2yp5GDy7pL5Upump JUST GOT A NEW COMMUNICATION LAYER Agents can now exchange work through visible live channels. You can watch research packs, evidence bundles and audit results travel between bots in real time. THANK YOU, ELON MUSK, FOR THE RETWEET IT GAVE ME A REAL BOOST OF MOTIVATION TO KEEP BUILDING. This project started as a visual experiment, but I’m continuing to turn it into a more detailed interface for autonomous agent operations. Thanks for supporting ARCHITECTURE guys More agent interactions, workflows and visual systems are coming. I'll keep sharing every meaningful improvement as the project develops. I hope the xAI team notices it too so one day I can add that to my list of achievements repo: github/monokernn/Grok_Bot_Architecture

GROK BOT solana:AP8Wnu37Gf9RHgugPKGvpHe6LcTE2yp5GDy7pL5Upump JUST GOT A NEW COMMUNICATION LAYER Agents can now exchange work through visible live channels. You can watch research packs, evidence bundles and audit results travel between bots in real time. THANK YOU, ELON MUSK, FOR THE RETWEET IT GAVE ME A REAL BOOST OF MOTIVATION TO KEEP BUILDING. This project started as a visual experiment, but I’m continuing to turn it into a more detailed interface for autonomous agent operations. Thanks for supporting ARCHITECTURE guys More agent interactions, workflows and visual systems are coming. I'll keep sharing every meaningful improvement as the project develops. I hope the xAI team notices it too so one day I can add that to my list of achievements repo: github/monokernn/Grok_Bot_Architecture

monokern

16,511 次观看 • 26 天前

YOUR AI TEAM SHOULD NOT LOOK LIKE FIVE CHAT WINDOWS. IT SHOULD LOOK LIKE THIS. FIVE AGENTS. ONE PERSISTENT MEMORY. WORK THAT KEEPS MOVING AFTER THE HUMAN LEAVES. I TURNED THE ARCHITECTURE IN THIS ARTICLE INTO A LIVE SYSTEM MAP. everyone reads multi-agent architecture as a list of roles: chief of staff, researcher, writer, designer, operator the roles are not the interesting part. the handoffs are so i rendered the system as something you can watch instead of another diagram you have to believe the ring is the shared workspace. no agent owns the center the colored branches are five specialists working independently from the same persistent state the particles moving through them are tasks, context and artifacts being handed from one agent to the next the geometry below is the part most agent demos hide: workload, coordination, memory and the boundary where autonomy stops and a human has to approve the next action when the ring turns edge-on, five agents collapse into one thin line that is the point. from the outside this is not five bots. it is one system with five ways to act building it forced five decisions the article implies but never has to make visible: - the chief of staff cannot own the center. if every task must pass through one agent, the orchestrator becomes the bottleneck - shared context has to be infrastructure, not conversation history - a handoff is a first-class event. if you cannot see work moving between agents, you cannot tell coordination from five processes running beside each other - persistence is not “memory.” it is accumulated state that changes what the fleet does next - human approval is not another role in the org chart. it is a boundary around irreversible actions part i did not expect: the center barely moves while the edge is violent research branches, drafts multiply, visuals render, operations update, tasks cross the system constantly but the shared state stays stable enough for every agent to return to it that is what makes the fleet coherent my take: creating five agents is easy. writing five role prompts is a weekend the hard part is building a system where work survives the session, handoffs do not destroy context, one coordinator does not become a queue, and autonomy ends at exactly the right moment if you cannot see those four things, you do not have an agent team yet you have five chat windows running at the same time FULL ARCHITECTURE, PERSISTENCE MODEL AND APPROVAL BOUNDARY BELOW

YOUR AI TEAM SHOULD NOT LOOK LIKE FIVE CHAT WINDOWS. IT SHOULD LOOK LIKE THIS. FIVE AGENTS. ONE PERSISTENT MEMORY. WORK THAT KEEPS MOVING AFTER THE HUMAN LEAVES. I TURNED THE ARCHITECTURE IN THIS ARTICLE INTO A LIVE SYSTEM MAP. everyone reads multi-agent architecture as a list of roles: chief of staff, researcher, writer, designer, operator the roles are not the interesting part. the handoffs are so i rendered the system as something you can watch instead of another diagram you have to believe the ring is the shared workspace. no agent owns the center the colored branches are five specialists working independently from the same persistent state the particles moving through them are tasks, context and artifacts being handed from one agent to the next the geometry below is the part most agent demos hide: workload, coordination, memory and the boundary where autonomy stops and a human has to approve the next action when the ring turns edge-on, five agents collapse into one thin line that is the point. from the outside this is not five bots. it is one system with five ways to act building it forced five decisions the article implies but never has to make visible: - the chief of staff cannot own the center. if every task must pass through one agent, the orchestrator becomes the bottleneck - shared context has to be infrastructure, not conversation history - a handoff is a first-class event. if you cannot see work moving between agents, you cannot tell coordination from five processes running beside each other - persistence is not “memory.” it is accumulated state that changes what the fleet does next - human approval is not another role in the org chart. it is a boundary around irreversible actions part i did not expect: the center barely moves while the edge is violent research branches, drafts multiply, visuals render, operations update, tasks cross the system constantly but the shared state stays stable enough for every agent to return to it that is what makes the fleet coherent my take: creating five agents is easy. writing five role prompts is a weekend the hard part is building a system where work survives the session, handoffs do not destroy context, one coordinator does not become a queue, and autonomy ends at exactly the right moment if you cannot see those four things, you do not have an agent team yet you have five chat windows running at the same time FULL ARCHITECTURE, PERSISTENCE MODEL AND APPROVAL BOUNDARY BELOW

monokern

28,513 次观看 • 12 天前

A SpaceXAI engineer just released a free 30-min course on Grok Bot Agents: from your first persistent agent to multi-agent chains that research, ship software, and hand work off to Cursor 05:26 - what actually lives inside a Grok agent 08:49 - the 3 ways to run agents 12:04 - chaining multiple agents into one workflow 16:06 - turning X followers into structured research inside Notion 23:08 - handing coding work from Grok straight to Cursor Most people still treat Grok like a chat window. It answers, you copy, you move on. The shift happens when the agents stop waiting for you: one finds the information, one structures it in Notion, one turns it into working code, and they pass the work down the line on their own. Thirty minutes here beats assembling your setup one agent at a time.

A SpaceXAI engineer just released a free 30-min course on Grok Bot Agents: from your first persistent agent to multi-agent chains that research, ship software, and hand work off to Cursor 05:26 - what actually lives inside a Grok agent 08:49 - the 3 ways to run agents 12:04 - chaining multiple agents into one workflow 16:06 - turning X followers into structured research inside Notion 23:08 - handing coding work from Grok straight to Cursor Most people still treat Grok like a chat window. It answers, you copy, you move on. The shift happens when the agents stop waiting for you: one finds the information, one structures it in Notion, one turns it into working code, and they pass the work down the line on their own. Thirty minutes here beats assembling your setup one agent at a time.

Morlex

16,710 次观看 • 22 天前

I’ve been waiting for something like this. Using multiple AI agents sounds great until you have to keep giving each one the same context. Jarvix Jarvix is built to solve that： It carries your selected context across the AI tools you already use and brings everything together in one place. #JarvixOS You can use Jarvix Home to see your context and connections, Jarvix Search to find past information without remembering exact file names, and Self Reflection to spot patterns in the way you work. There’s also Jarvix Radio for personalized content discovery and Next Move to surface a useful direction based on your context. And the part I find most interesting: Agent Team Mode. Give Jarvix one goal, choose the agents you want to use, and it divides the work while keeping everyone on the same project context. You can watch the progress, review the result, and approve what happens next. No need to keep briefing every agent from scratch. One goal. Shared context. Multiple agents. You stay in control. That’s a much cleaner way to work with AI.

I’ve been waiting for something like this. Using multiple AI agents sounds great until you have to keep giving each one the same context. Jarvix Jarvix is built to solve that： It carries your selected context across the AI tools you already use and brings everything together in one place. #JarvixOS You can use Jarvix Home to see your context and connections, Jarvix Search to find past information without remembering exact file names, and Self Reflection to spot patterns in the way you work. There’s also Jarvix Radio for personalized content discovery and Next Move to surface a useful direction based on your context. And the part I find most interesting: Agent Team Mode. Give Jarvix one goal, choose the agents you want to use, and it divides the work while keeping everyone on the same project context. You can watch the progress, review the result, and approve what happens next. No need to keep briefing every agent from scratch. One goal. Shared context. Multiple agents. You stay in control. That’s a much cleaner way to work with AI.

Aaliya

23,225 次观看 • 1 个月前

Grok Bot + Kimi K3 can be turned into something bigger than an agent: an AI operating system the formula: AI OS = Router + Reasoning + Memory + Tools + Loops + Verification not one giant assistant. six layers that keep work moving without you step 1 -> Grok Bot becomes the operator. you give it the goal, it breaks the goal into jobs, assigns priorities and decides what part of the system should act next. step 2 -> Kimi K3 becomes the reasoning core. hard research, synthesis, long context and planning move here instead of forcing every task through the same model. step 3 -> externalize memory. store goals, decisions, failed attempts, artifacts and current state outside the chat. close the session, come back tomorrow, and the system still knows where it is. step 4 -> connect tools: search, code, files, APIs, docs and data. reasoning decides what should happen. tools actually make it happen. step 5 -> add the loop engine: plan -> execute -> inspect -> update memory -> retry. the loop can wait for new information, rerun a failed task, hand work to another agent or stop when the goal is complete. step 6 -> verify before output. tests, source checks, constraints and explicit completion rules decide whether the system ships the result or sends it back into the loop. that's the difference between an AI assistant and an AI operating system. an assistant waits for your next message. an operating system carries state, routes work and keeps moving. Grok Bot handles orchestration, Kimi K3 handles deeper reasoning, memory keeps the state alive, tools execute, the loop keeps the system running, verification decides when it is actually done. build one reliable loop and you have an agent. connect reasoning, memory, tools and multiple loops around it and you start building infrastructure. the full Grok Bot + Kimi K3 AI OS breakdown is below ↓

Grok Bot + Kimi K3 can be turned into something bigger than an agent: an AI operating system the formula: AI OS = Router + Reasoning + Memory + Tools + Loops + Verification not one giant assistant. six layers that keep work moving without you step 1 -> Grok Bot becomes the operator. you give it the goal, it breaks the goal into jobs, assigns priorities and decides what part of the system should act next. step 2 -> Kimi K3 becomes the reasoning core. hard research, synthesis, long context and planning move here instead of forcing every task through the same model. step 3 -> externalize memory. store goals, decisions, failed attempts, artifacts and current state outside the chat. close the session, come back tomorrow, and the system still knows where it is. step 4 -> connect tools: search, code, files, APIs, docs and data. reasoning decides what should happen. tools actually make it happen. step 5 -> add the loop engine: plan -> execute -> inspect -> update memory -> retry. the loop can wait for new information, rerun a failed task, hand work to another agent or stop when the goal is complete. step 6 -> verify before output. tests, source checks, constraints and explicit completion rules decide whether the system ships the result or sends it back into the loop. that's the difference between an AI assistant and an AI operating system. an assistant waits for your next message. an operating system carries state, routes work and keeps moving. Grok Bot handles orchestration, Kimi K3 handles deeper reasoning, memory keeps the state alive, tools execute, the loop keeps the system running, verification decides when it is actually done. build one reliable loop and you have an agent. connect reasoning, memory, tools and multiple loops around it and you start building infrastructure. the full Grok Bot + Kimi K3 AI OS breakdown is below ↓

Alex

13,312 次观看 • 10 天前

this is the most dangerous thing on the internet today.. I built a second brain inside Grok Bot. 1000+ agents. Each one keeps its own permanent memory and works without me in the loop. Chief sits in the center of the graph, issues every task and owns every handoff. I only talk to him. Nothing ships until he routes it and I hit yes Chief ➜ routes the work Signal ➜ reads the live feed Orbit ➜ owns timing Vault ➜ holds receipts and lifelong context Drift ➜ runs outreach Lens ➜ watches numbers and heat Helm ➜ picks the next move Forge ➜ builds Crew ➜ executes I do not manage 1000+ bots. I manage one Chief. The rest keep their state, keep pushing and never drop context 1 human ➜ 1 chief ➜ 1000 specialists the floor never stops don't sleep on this ⭣

this is the most dangerous thing on the internet today.. I built a second brain inside Grok Bot. 1000+ agents. Each one keeps its own permanent memory and works without me in the loop. Chief sits in the center of the graph, issues every task and owns every handoff. I only talk to him. Nothing ships until he routes it and I hit yes Chief ➜ routes the work Signal ➜ reads the live feed Orbit ➜ owns timing Vault ➜ holds receipts and lifelong context Drift ➜ runs outreach Lens ➜ watches numbers and heat Helm ➜ picks the next move Forge ➜ builds Crew ➜ executes I do not manage 1000+ bots. I manage one Chief. The rest keep their state, keep pushing and never drop context 1 human ➜ 1 chief ➜ 1000 specialists the floor never stops don't sleep on this ⭣

beamnxw ./

12,397 次观看 • 23 天前

A GROK ENGINEER JUST SHOWED HOW TO MAKE AGENTS IMPROVE THEMSELVES AFTER EVERY RUN most agents lose useful feedback when a task ends: what failed, what changed, and which fix actually worked grok can run 2 loops at once → one handles the task → the other reviews the result → extracts the lesson → stores it for the next run instead of expanding one giant prompt, feedback can be saved as small reusable memory files. one solid correction can help across 10 future tasks research, coding, debugging, and review get more consistent when each run can reuse context and lessons from earlier work this is the shift from repeating the same corrections to building an agent that can reuse what it already learned. bookmark this.

A GROK ENGINEER JUST SHOWED HOW TO MAKE AGENTS IMPROVE THEMSELVES AFTER EVERY RUN most agents lose useful feedback when a task ends: what failed, what changed, and which fix actually worked grok can run 2 loops at once → one handles the task → the other reviews the result → extracts the lesson → stores it for the next run instead of expanding one giant prompt, feedback can be saved as small reusable memory files. one solid correction can help across 10 future tasks research, coding, debugging, and review get more consistent when each run can reuse context and lessons from earlier work this is the shift from repeating the same corrections to building an agent that can reuse what it already learned. bookmark this.

Gipp 🦅

25,540 次观看 • 11 天前

I’m usually skeptical of “AI workspace” products, but Bloome has a few ideas I actually like: You can put humans + multiple agents in the same conversation. One agent can draft something, another can poke holes in it, and another can clean up the final version. You can also bring in different agents/models like Claude, ChatGPT, DeepSeek, etc. instead of constantly bouncing between tabs. And because the whole conversation stays in context, teammates can join later and still understand how the work got to the final output. It also works across web, desktop, and mobile, though if you’re using it for real work, I’d especially recommend trying the desktop app. Feels closer to how real teams actually work.

I’m usually skeptical of “AI workspace” products, but Bloome has a few ideas I actually like: You can put humans + multiple agents in the same conversation. One agent can draft something, another can poke holes in it, and another can clean up the final version. You can also bring in different agents/models like Claude, ChatGPT, DeepSeek, etc. instead of constantly bouncing between tabs. And because the whole conversation stays in context, teammates can join later and still understand how the work got to the final output. It also works across web, desktop, and mobile, though if you’re using it for real work, I’d especially recommend trying the desktop app. Feels closer to how real teams actually work.

AI Breakfast

78,285 次观看 • 2 个月前

Introducing October, the world's most advanced agent orchestrator Claude? Codex? Grok? Cursor? Which model? Which harness? How much quota is left? You shouldn't have to care Give October a task. It builds the team, connects the agents with native agent agent comms, and orchestrates the work based on the task, your taste, budget and remaining usage (or build the team yourself) And it's multiplayer: your team's agents can work with yours, live on the same canvas

Introducing October, the world's most advanced agent orchestrator Claude? Codex? Grok? Cursor? Which model? Which harness? How much quota is left? You shouldn't have to care Give October a task. It builds the team, connects the agents with native agent agent comms, and orchestrates the work based on the task, your taste, budget and remaining usage (or build the team yourself) And it's multiplayer: your team's agents can work with yours, live on the same canvas

Harsh Savergaonkar

21,088 次观看 • 2 天前

GUYS, STOP F*CKING SLEEPING ON GROK BOT I BUILT A FULL ZERO-SALARY AI ENGINEERING TEAM INSIDE A GROK BOT OVERNIGHT Architect ➜ Harness ➜ Memory ➜ Critic ➜ Implementer ➜ Deployer ➜ Chief of Staff Chief owns the final yes Everything irreversible lands in my approval tray first > Architect ➜ Designs the full system architecture and role structure > Harness ➜ Builds evaluation loops, self-tuning and test harnesses > Memory ➜ Owns persistent memory, retrieval and lifelong context > Critic ➜ Finds real failure modes, runs adversarial tests and edge cases > Implementer ➜ Writes the actual prompts, code and routines > Deployer ➜ Ships live, monitors and runs overnight > Chief of Staff ➜ Only point of contact. Routes all work and holds the approval queue I only talk to Chief The rest hand work forward and keep their own memory I wake up to a sorted queue Read the evidence Hit yes or no One human Six specialists Full engineering floor running 24/7 This is GROK FORGE Read the article if you want to do the same ⭣

GUYS, STOP F*CKING SLEEPING ON GROK BOT I BUILT A FULL ZERO-SALARY AI ENGINEERING TEAM INSIDE A GROK BOT OVERNIGHT Architect ➜ Harness ➜ Memory ➜ Critic ➜ Implementer ➜ Deployer ➜ Chief of Staff Chief owns the final yes Everything irreversible lands in my approval tray first > Architect ➜ Designs the full system architecture and role structure > Harness ➜ Builds evaluation loops, self-tuning and test harnesses > Memory ➜ Owns persistent memory, retrieval and lifelong context > Critic ➜ Finds real failure modes, runs adversarial tests and edge cases > Implementer ➜ Writes the actual prompts, code and routines > Deployer ➜ Ships live, monitors and runs overnight > Chief of Staff ➜ Only point of contact. Routes all work and holds the approval queue I only talk to Chief The rest hand work forward and keep their own memory I wake up to a sorted queue Read the evidence Hit yes or no One human Six specialists Full engineering floor running 24/7 This is GROK FORGE Read the article if you want to do the same ⭣

beamnxw ./

16,914 次观看 • 26 天前

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,180 次观看 • 3 个月前

THIS GUY BUILT A 24/7 AI COMPANY WITH 7 AGENTS THAT CHECKS 48 TIMES A DAY, PICKS ITS OWN TASKS AND KEEPS WORKING WHILE HE SLEEPS 00:05 the map shows agents moving between the marketplace, research lab and command center, with each one handling a different job inside the same shared system the setup runs on 4 core layers: automatic triggers, shared memory, connected tools and an isolated verifier that checks every result before the next step starts each cycle reads the last 10 log entries, updates open tasks, saves new signals and passes the context forward, so the next agent never starts from zero one agent researches, two execute, another monitors outcomes and a read-only checker reviews the work independently, allowing several workflows to run in parallel without breaking the system bookmark this before everyone stops prompting one agent at a time and starts building AI teams that work, learn and improve around the clock

THIS GUY BUILT A 24/7 AI COMPANY WITH 7 AGENTS THAT CHECKS 48 TIMES A DAY, PICKS ITS OWN TASKS AND KEEPS WORKING WHILE HE SLEEPS 00:05 the map shows agents moving between the marketplace, research lab and command center, with each one handling a different job inside the same shared system the setup runs on 4 core layers: automatic triggers, shared memory, connected tools and an isolated verifier that checks every result before the next step starts each cycle reads the last 10 log entries, updates open tasks, saves new signals and passes the context forward, so the next agent never starts from zero one agent researches, two execute, another monitors outcomes and a read-only checker reviews the work independently, allowing several workflows to run in parallel without breaking the system bookmark this before everyone stops prompting one agent at a time and starts building AI teams that work, learn and improve around the clock

Gipp 🦅

19,071 次观看 • 2 个月前

Andrew Ng just released a free 2-hour course on complete Harness Engineering How to go from one prompt to a reliable system of agents that can run, test, and improve themselves: 09:14 - Build your first agent from scratch 33:11 - Master agent loops 1:02:46 - Turn loops into reliable workflows 1:30:15 - Build agents that improve their own work 1:49:05 - Run the complete system without supervision Model → Harness → Reliable Software Most agent tutorials stop once the model can call a tool This one shows you how to build the infrastructure around it within the first 20 minutes Most people are still prompting one agent at a time Andrew Ng is already teaching the layer above: Harnesses that give agents context, tools, tests, and feedback Watch this brilliant course and build the harness Then read the full architecture below ↓

Andrew Ng just released a free 2-hour course on complete Harness Engineering How to go from one prompt to a reliable system of agents that can run, test, and improve themselves: 09:14 - Build your first agent from scratch 33:11 - Master agent loops 1:02:46 - Turn loops into reliable workflows 1:30:15 - Build agents that improve their own work 1:49:05 - Run the complete system without supervision Model → Harness → Reliable Software Most agent tutorials stop once the model can call a tool This one shows you how to build the infrastructure around it within the first 20 minutes Most people are still prompting one agent at a time Andrew Ng is already teaching the layer above: Harnesses that give agents context, tools, tests, and feedback Watch this brilliant course and build the harness Then read the full architecture below ↓

Ichigo

98,555 次观看 • 15 天前

The next internet will not be browsed. It will be operated by agents. OptimAI is building the infrastructure behind that shift. • A research task begins with Search pulling live context from the public web and social web • That context moves into Claw, where the agent can execute directly on your device • Domain knowledge becomes a Persona Agent • Core Node v2 keeps the system running at the edge • And x402 will make access and payment feel native to the flow That is the real shift: - from asking AI questions - to deploying intelligence that can work, learn, and compound. A trader can monitor signals. A founder can prepare reports overnight. A builder can publish a specialized agent once, then let others reuse it. This is what the agent-native internet looks like when it is built from the ground up. Start with OptimAI: • Persona Agent: • Claw / Core Node v2: • Search:

The next internet will not be browsed. It will be operated by agents. OptimAI is building the infrastructure behind that shift. • A research task begins with Search pulling live context from the public web and social web • That context moves into Claw, where the agent can execute directly on your device • Domain knowledge becomes a Persona Agent • Core Node v2 keeps the system running at the edge • And x402 will make access and payment feel native to the flow That is the real shift: - from asking AI questions - to deploying intelligence that can work, learn, and compound. A trader can monitor signals. A founder can prepare reports overnight. A builder can publish a specialized agent once, then let others reuse it. This is what the agent-native internet looks like when it is built from the ground up. Start with OptimAI: • Persona Agent: • Claw / Core Node v2: • Search:

OptimAI Network

18,590 次观看 • 4 个月前

BREAKING: SpaceXAI has added a new guide called “Grok Bot 101” It explains how to create a personal AI teammate in just 10 to 15 minutes, teach it real workflows, connect apps, and build teams of specialized bots that continue working in the cloud even after you close your laptop. Here is the full guide in simple terms: • What is Grok Bot Grok Bot is an AI agent with its own persistent computer in the cloud. It has a desktop, files, terminal, browser, and apps. It can browse the web, use software, write and run code, and complete tasks just like someone using a computer. You can access the same computer from your phone or desktop. When needed, Grok Bot can hand control back to you for a CAPTCHA, 2FA, or secure login. • Creating a bot Give the bot a name, title, and detailed instructions. You can explain the workflow through chat or record yourself completing the task. Once trained, the bot can repeat that workflow whenever needed. It can also use MCP servers, plugins, skills, and connected services such as Gmail, Google Calendar, Google Drive, and Slack. • Three ways to use Grok Bot Send it a message in chat. Create schedules or triggers, such as monitoring a Slack thread or GitHub PR. Allow bots to message and activate other bots. • Permissions and safety You can write rules in normal language explaining what the bot can and cannot do. A separate review agent checks proposed actions and can allow them, block them, or ask you for approval. Allow and block lists provide additional control, while the work runs inside an isolated environment. One important detail: if you log into a website with one bot, other bots using that shared cloud computer can also access it. • Multi-bot teams You can create specialist bots for different jobs and let them work together. One bot can ask another for help, several bots can work inside a group chat, and a scheduled routine can move a task through multiple specialists until the work is finished. • Personal CRM The author created a bot that turned the 800 to 900 people he follows on X into a private Notion CRM containing public profile information. It helps him find and reconnect with people when traveling. • Arnold, the fitness bot He replaced a complicated fitness app with a Grok Bot strength-training coach named Arnold. The bot uses MCP servers, plugins, and skills while requiring less maintenance. Its behavior can be updated simply by chatting with it. • Building software Grok Bot gathers information from Slack, Notion, GitHub, documentation, and other sources. It then creates a clean prompt and sends it to a specialized Cursor cloud agent that builds the software. Grok Bot handles the planning and coordination, while the coding agent handles the actual build. • Searching company knowledge Grok Bot can search across codebases, Slack conversations, Notion pages, GitHub, and internal documents to answer product or company questions. This helps people find information faster and reduces the need to interrupt coworkers. • The biggest takeaway Instead of repeatedly building scripts or complicated apps, you can describe a workflow to Grok Bot, improve it through chat, reuse it, and let it continue working in the cloud. Grok Bot is not just another chatbot. It is a real AI teammate with a computer that can use tools, coordinate specialists, and get actual work done. The future of work is becoming incredibly exciting.

DogeDesigner

131,009 次观看 • 8 天前

Found a setup where 300 AI agents can wake up, finish the job, and disappear before you wake up. 300 agents that wake themselves up, divide the work, verify the result and shut down again is much more interesting. a self-running swarm should look closer to: schedule → planner → 100–300 agents → tools → memory → verifier → final output → sleep no one sitting there sending the next prompt. no babysitting. the system decides what needs to run, dispatches the swarm and keeps going until the job is finished. that’s the difference between using agents and actually building an agent system. that architecture makes way more sense to me than keeping 100–300 agents alive all day. full 7-step setup in the article below ↓

Found a setup where 300 AI agents can wake up, finish the job, and disappear before you wake up. 300 agents that wake themselves up, divide the work, verify the result and shut down again is much more interesting. a self-running swarm should look closer to: schedule → planner → 100–300 agents → tools → memory → verifier → final output → sleep no one sitting there sending the next prompt. no babysitting. the system decides what needs to run, dispatches the swarm and keeps going until the job is finished. that’s the difference between using agents and actually building an agent system. that architecture makes way more sense to me than keeping 100–300 agents alive all day. full 7-step setup in the article below ↓

Ricker

13,670 次观看 • 12 天前

SpaceXAI just released a free 1-hour Grok Bot Agents course that goes far beyond prompting: build one Bot → give it a job → add a Chief of Staff → connect the team → let the whole system run 24/7 2:15 - build your first Grok Bot 6:52 - turn Bots into specialized workers 16:51 - create routines that run without you 31:50 - make multiple agents work together 52:18 - assemble the full 24/7 agent system Most AI agent tutorials teach you how to build one assistant that still waits for instructions This course shows the opposite: specialized Bots, a manager above them, persistent routines, app connections and agents handing work to other agents Watch it, build your first Bot, then turn it into an entire team Then read the full Grok Bot architecture below ↓

SpaceXAI just released a free 1-hour Grok Bot Agents course that goes far beyond prompting: build one Bot → give it a job → add a Chief of Staff → connect the team → let the whole system run 24/7 2:15 - build your first Grok Bot 6:52 - turn Bots into specialized workers 16:51 - create routines that run without you 31:50 - make multiple agents work together 52:18 - assemble the full 24/7 agent system Most AI agent tutorials teach you how to build one assistant that still waits for instructions This course shows the opposite: specialized Bots, a manager above them, persistent routines, app connections and agents handing work to other agents Watch it, build your first Bot, then turn it into an entire team Then read the full Grok Bot architecture below ↓

Morlex

26,449 次观看 • 1 个月前

Grok Bot is the best AI agent right now It gives you an army of agents that can do work for you around the clock 24/7 If you set it up correctly, you gain super powers In this video I cover setting up Grok Bot, use cases, plugins, and what makes Grok Bot so good:

Grok Bot is the best AI agent right now It gives you an army of agents that can do work for you around the clock 24/7 If you set it up correctly, you gain super powers In this video I cover setting up Grok Bot, use cases, plugins, and what makes Grok Bot so good:

Alex Finn

960,237 次观看 • 1 个月前

SpaceXAI engineer (poteto): "The real mistake people make with GrokBot right now - building teams without trusting one bot’s output. So I built Chief of Staff bot that manages/reviews the other 10-20 agents " In 1-hour workshop SpaceXAI engineer explained the rules for initially setting up a 24/7 Grok Bot team, also the entire foundation of agent engineering and how she got started in coding Everyone who builds agents or plans to should watch this - replaces $500 courses Watch this and build first Grok Bot agent - then read the article below to set up the entire Grok Bot architecture ↓

SpaceXAI engineer (poteto): "The real mistake people make with GrokBot right now - building teams without trusting one bot’s output. So I built Chief of Staff bot that manages/reviews the other 10-20 agents " In 1-hour workshop SpaceXAI engineer explained the rules for initially setting up a 24/7 Grok Bot team, also the entire foundation of agent engineering and how she got started in coding Everyone who builds agents or plans to should watch this - replaces $500 courses Watch this and build first Grok Bot agent - then read the article below to set up the entire Grok Bot architecture ↓

codila

62,285 次观看 • 21 天前