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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 ↓show more

Alex

3,381 subscribers

13,078 просмотров • 2 дней назад •via X (Twitter)

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Комментарии: 16

Фото профиля Morlex

Morlex2 дней назад

brilliant work broski. booked

Фото профиля Argona

Argona2 дней назад

damn the verification layer is smart

Фото профиля Alex

Alex2 дней назад

need to try this

Фото профиля NiNE

NiNE2 дней назад

Verification as its own layer is the part most desks skip. On mine: kill-switch + a second reader that never grades its own homework. Router/tools/loops without verify just ship confident mistakes faster. Name the verify step before you stack another model.

Фото профиля William G Strouse, CHRISTIAN, Retired!

William G Strouse, CHRISTIAN, Retired!2 дней назад

So the goal is for the bot OS to make decisions that the operator has not regardless of why? WHO oversees the bot OS? who can shut it off?

Фото профиля Cryptoartmaster

Cryptoartmaster2 дней назад

Grok handling verification is critical for stopping hallucinations in multi-step workflows.

Фото профиля Jonathan

Jonathan2 дней назад

What do you actually gain from this setup? are you running this personally?

Фото профиля Wallchain Community Hub

Wallchain Community Hub2 дней назад

this setup actually changes everything about how people build

Фото профиля Money Bunny

Money Bunny1 день назад

Stacking these layers actually makes the whole system self correcting over time

Фото профиля Filosof

Filosof1 день назад

The layer missing here is the one a real OS actually has. Not memory, memory management. Store goals, decisions, failed attempts and artifacts long enough and the reasoning core ends up planning against a state that is mostly its own failures. Persistence without an eviction policy is not memory, it is a log that eventually becomes the whole prompt.

Фото профиля Sevenup

Sevenup2 дней назад

This is the right way to think about it. The breakthrough isn’t “Grok + Kimi.” It’s the architecture around them.

Фото профиля Alex

Alex2 дней назад

this is the best setup right now if you ask me

Фото профиля Mahax

Mahax2 дней назад

steps are useful, tho you need some ideas to implement that system

Фото профиля Alex

Alex2 дней назад

of course, without an idea, it's all pointless

Фото профиля MAX

MAX2 дней назад

Memory is the part most agent setups still miss.

Фото профиля Alex

Alex2 дней назад

yeah, is very important part

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kocer

30,403 просмотров • 2 месяцев назад

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.

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Dami-Defi

13,354 просмотров • 2 месяцев назад

"Everyone's talking about continual learning. That's entirely where this space is going to go." The Applied Compute platform is architected around that premise: build memory and intuition from fragmented data across your entire org, train reasoning models directly on top of it, and close the loop. A model is just one piece. An agent is where it runs, what tools it has, how permissions and auth are handled, how humans guide and instruct it, and the observability around it all. Every interaction should be treated as a training signal so the system can compound over time. Thanks for having us TBPN

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Applied Compute

64,309 просмотров • 6 месяцев назад

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Andrew Ng

131,965 просмотров • 1 год назад

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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

13,508 просмотров • 4 дней назад

Anthropic hired this engineer at $250K-$750K a year because he knows how to build harnesses for multi-agent systems In this 15-minute workshop, he shows exactly how to build one from scratch AI → Agents → Harness → Loops → Graphs step 1 → start with the Claude Agent SDK - the harness handles loops, context, and sandboxing step 2 → separate the brain from the hands - reasoning in one place, tools in a sandbox, 60% faster to first token step 3 → run it server-side and log every step - close your laptop and it keeps running, crashes resume from the log step 4 → make failure cheap - retry dead sandboxes and replay lost context instead of starting over step 5 → turn yesterday's logs into new memory and skills - the harness wakes up smarter Anthropic calls this "dreaming" Most people spend weeks building this by hand You don't have to Bookmark and watch it Then read the full harness engineering guide below ↓

Anthropic hired this engineer at $250K-$750K a year because he knows how to build harnesses for multi-agent systems In this 15-minute workshop, he shows exactly how to build one from scratch AI → Agents → Harness → Loops → Graphs step 1 → start with the Claude Agent SDK - the harness handles loops, context, and sandboxing step 2 → separate the brain from the hands - reasoning in one place, tools in a sandbox, 60% faster to first token step 3 → run it server-side and log every step - close your laptop and it keeps running, crashes resume from the log step 4 → make failure cheap - retry dead sandboxes and replay lost context instead of starting over step 5 → turn yesterday's logs into new memory and skills - the harness wakes up smarter Anthropic calls this "dreaming" Most people spend weeks building this by hand You don't have to Bookmark and watch it Then read the full harness engineering guide below ↓

rari

182,266 просмотров • 9 дней назад

Google Brain founder, Andrew Ng: "One of the most important skills of the future is the ability to tell a computer exactly what you want so it can do it for you." In this talk, he explains why the biggest opportunity isn't just using AI. It's learning how to turn work into agentic workflows, break tasks into steps, give agents the right tools and context, add evals, and make the whole system reliable. That's the difference between prompting an AI and engineering an agent that actually works. Watch it today, then read the full guide in the article below.

Google Brain founder, Andrew Ng: "One of the most important skills of the future is the ability to tell a computer exactly what you want so it can do it for you." In this talk, he explains why the biggest opportunity isn't just using AI. It's learning how to turn work into agentic workflows, break tasks into steps, give agents the right tools and context, add evals, and make the whole system reliable. That's the difference between prompting an AI and engineering an agent that actually works. Watch it today, then read the full guide in the article below.

Rahul

14,431 просмотров • 1 месяц назад

MOST PEOPLE USE CLAUDE CODE LIKE CHATGPT WITH A TERMINAL That is why they only get 30% of the value. They paste a task, wait for an answer, then keep explaining the same rules again when the context gets messy. The real power starts when you stop treating it like a chatbot and start treating it like an operating system for work. The useful commands are not flashy. They just remove the parts that quietly waste hours: > /init builds the project memory > /memory edits the rules Claude should keep > /clear resets the chat without losing the setup > /compact compresses a long session before quality drops > /context shows where your tokens are going > /rewind rolls back when an edit breaks the direction > /plan forces a blueprint before execution > /model matches the brain to the task > /goal lets it work toward a clear finish line This is the difference between asking AI for help and actually managing an AI worker. One mode gives you random useful answers. The other gives you memory, boundaries, checkpoints, context control and repeatable execution. Most people are still trying to write better prompts. The better move is building a better system around the model. save this

MOST PEOPLE USE CLAUDE CODE LIKE CHATGPT WITH A TERMINAL That is why they only get 30% of the value. They paste a task, wait for an answer, then keep explaining the same rules again when the context gets messy. The real power starts when you stop treating it like a chatbot and start treating it like an operating system for work. The useful commands are not flashy. They just remove the parts that quietly waste hours: > /init builds the project memory > /memory edits the rules Claude should keep > /clear resets the chat without losing the setup > /compact compresses a long session before quality drops > /context shows where your tokens are going > /rewind rolls back when an edit breaks the direction > /plan forces a blueprint before execution > /model matches the brain to the task > /goal lets it work toward a clear finish line This is the difference between asking AI for help and actually managing an AI worker. One mode gives you random useful answers. The other gives you memory, boundaries, checkpoints, context control and repeatable execution. Most people are still trying to write better prompts. The better move is building a better system around the model. save this

Asteri

15,347 просмотров • 2 месяцев назад

I launched Grok Bot for 24 hours, gave it 3 jobs, and stopped treating AI like a chatbot. I had it scan live X + web data for emerging AI products, research what people were actually saying about them, filter the obvious engagement bait, compare sources and turn the strongest signals into a structured brief. At the same time, I could have separate research paths looking at competitors, code or market opportunities instead of forcing everything through one conversation. That’s the difference I underestimated. I don’t want to sit inside a chat writing another prompt every 5 minutes. I want to define the objective, give the agent access to the right tools and data, let it execute, then step back in when judgment or approval is actually needed. The old workflow was prompt → answer → copy → repeat. The workflow I’m moving toward is assign → execute → verify. Once AI stops waiting for your next message, it stops feeling like software you use and starts feeling like work you delegated.

I launched Grok Bot for 24 hours, gave it 3 jobs, and stopped treating AI like a chatbot. I had it scan live X + web data for emerging AI products, research what people were actually saying about them, filter the obvious engagement bait, compare sources and turn the strongest signals into a structured brief. At the same time, I could have separate research paths looking at competitors, code or market opportunities instead of forcing everything through one conversation. That’s the difference I underestimated. I don’t want to sit inside a chat writing another prompt every 5 minutes. I want to define the objective, give the agent access to the right tools and data, let it execute, then step back in when judgment or approval is actually needed. The old workflow was prompt → answer → copy → repeat. The workflow I’m moving toward is assign → execute → verify. Once AI stops waiting for your next message, it stops feeling like software you use and starts feeling like work you delegated.

Lummox

3,935,757 просмотров • 21 дней назад

HOW TO USE AI LOOPS TO RUN YOUR BUSINESS 24/7 A lot has been written about loop engineering for building products. Almost nothing about using loops to run the business itself. That's the bigger idea. A loop is when you give an agent a goal, a way to check its own work, and permission to keep trying until it hits that goal. Build. Verify. Repeat. Stop when the condition is met. Here's what it looks like in practice: 1/SEO loop You're position 30 for a term you want. The loop runs once a month, makes changes, checks where you rank, and keeps pushing until you're on page one. This is running in production right now on Inbox Zero. 2/Ads loop You're spending $100 a day and losing money. The loop tests creative, checks profitability, kills what fails, and keeps going until the account is in the black. 3/Eval loop Your AI feature is only 88% accurate. The loop keeps adjusting the prompt and swapping the model until it passes 90%. 4/LLM visibility loop People search in ChatGPT now, not just Google. Same loop, new scoreboard. Are we the answer or not? The whole thing hinges on one thing: a metric that comes back black and white. Where do I rank? Did it hit profitability? Did the evals pass? Give an agent that scoreboard and it runs for months. Loops used to run for 30 minutes. These run for a year. Take a step, sleep, wake up next month, take another one. You're basically hiring an agency that never sleeps, gets paid in tokens instead of invoices, and undoes its own mistakes when the number goes down. Full episode on The Startup Ideas Podcast (SIP) 🧃 watch

HOW TO USE AI LOOPS TO RUN YOUR BUSINESS 24/7 A lot has been written about loop engineering for building products. Almost nothing about using loops to run the business itself. That's the bigger idea. A loop is when you give an agent a goal, a way to check its own work, and permission to keep trying until it hits that goal. Build. Verify. Repeat. Stop when the condition is met. Here's what it looks like in practice: 1/SEO loop You're position 30 for a term you want. The loop runs once a month, makes changes, checks where you rank, and keeps pushing until you're on page one. This is running in production right now on Inbox Zero. 2/Ads loop You're spending $100 a day and losing money. The loop tests creative, checks profitability, kills what fails, and keeps going until the account is in the black. 3/Eval loop Your AI feature is only 88% accurate. The loop keeps adjusting the prompt and swapping the model until it passes 90%. 4/LLM visibility loop People search in ChatGPT now, not just Google. Same loop, new scoreboard. Are we the answer or not? The whole thing hinges on one thing: a metric that comes back black and white. Where do I rank? Did it hit profitability? Did the evals pass? Give an agent that scoreboard and it runs for months. Loops used to run for 30 minutes. These run for a year. Take a step, sleep, wake up next month, take another one. You're basically hiring an agency that never sleeps, gets paid in tokens instead of invoices, and undoes its own mistakes when the number goes down. Full episode on The Startup Ideas Podcast (SIP) 🧃 watch

GREG ISENBERG

83,210 просмотров • 2 месяцев назад

holy sh*t this grok bot repo is f**king insane xai basically published the building blocks for turning grok from a chatbot into an actual agent [official repo with 560+ stars] 1/ give grok tools and function calls 2/ connect web + x search 3/ add code execution and memory the repo also shows real examples for agents, voice, files and multi-step workflows instead of just api hello world demos this is probably the fastest way to understand how xai expects grok bot systems to actually be built

holy sh*t this grok bot repo is f**king insane xai basically published the building blocks for turning grok from a chatbot into an actual agent [official repo with 560+ stars] 1/ give grok tools and function calls 2/ connect web + x search 3/ add code execution and memory the repo also shows real examples for agents, voice, files and multi-step workflows instead of just api hello world demos this is probably the fastest way to understand how xai expects grok bot systems to actually be built

Gipp 🦅

17,360 просмотров • 8 дней назад

Godfather of agents showed how he created the loop engineering that everyone now uses: as PhD student, he wrote the formula every AI agent now runs on - his formula taught agents to self-improve here's the formula: step 1 → stop making the model just think OR just act - make it alternate: think → act → observe → think again step 2 → let it reason out loud before every action - the plan guides the next move, not just the output step 3 → feed the result of each action back in - the model reads what happened and adjusts, live step 4 → when it fails, don't restart make it reflect on WHY it failed, in words, then retry with that memory step 5 → store those reflections - the agent gets better across attempts without any retraining before OpenAI, before he ran Tencent's AI - he was a grad student who quietly wrote the blueprint every agent now follows - now give 90-min course watch & bookmark - then read how the agent systems actually works ↓

Godfather of agents showed how he created the loop engineering that everyone now uses: as PhD student, he wrote the formula every AI agent now runs on - his formula taught agents to self-improve here's the formula: step 1 → stop making the model just think OR just act - make it alternate: think → act → observe → think again step 2 → let it reason out loud before every action - the plan guides the next move, not just the output step 3 → feed the result of each action back in - the model reads what happened and adjusts, live step 4 → when it fails, don't restart make it reflect on WHY it failed, in words, then retry with that memory step 5 → store those reflections - the agent gets better across attempts without any retraining before OpenAI, before he ran Tencent's AI - he was a grad student who quietly wrote the blueprint every agent now follows - now give 90-min course watch & bookmark - then read how the agent systems actually works ↓

codila

28,667 просмотров • 1 месяц назад

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 просмотров • 21 дней назад

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

66,501 просмотров • 9 часов назад