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If you shipped an AI agent this year, good luck getting it found. Agent directories skip MCP servers. MCP registries skip agents. Skills are scattered across all of them. So the whole space gets discovered by accident: > a GitHub readme > a reply under someone else's post >...

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Nick Launches

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121,786 views • 27 days ago •via X (Twitter)

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300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.
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300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

shmidt

19,505 views • 2 months ago

Zuckerberg built his own AI agent to run Meta. this man is literally becoming Tony Stark. it pulls data from every team inside the company so he can skip meetings, skip the chain of command, and make decisions faster than any human process allows. 78,000 employees have their own AI agents now too. one messages coworkers on your behalf. another acts as your AI chief of staff. their agents talk to each other in an internal network. humans optional. Meta also bought an entire social media platform built for AI agents to interact with each other. read that again. Zuck said he wants every person at Meta to have a personal AI agent. then every person outside Meta. the Jarvis era started.
0:13

Zuckerberg built his own AI agent to run Meta. this man is literally becoming Tony Stark. it pulls data from every team inside the company so he can skip meetings, skip the chain of command, and make decisions faster than any human process allows. 78,000 employees have their own AI agents now too. one messages coworkers on your behalf. another acts as your AI chief of staff. their agents talk to each other in an internal network. humans optional. Meta also bought an entire social media platform built for AI agents to interact with each other. read that again. Zuck said he wants every person at Meta to have a personal AI agent. then every person outside Meta. the Jarvis era started.

sui

153,670 views • 6 months ago

This is genuinely insane 🤯 I sent the same prompt to 2 Claude Opus 5 agents 1 agent used no MCPs 1 agent used 2 MCPS the goal was to clone a very famous game, that has over 4 billion downloads and the results are scary.. The 1st agents game looks pretty basic. but has all the mechanics in place and even has power ups. The 2nd agents game looks polished especially for 1 single prompt. the 2nd agent used higgsfield mcp to create images for all of the assets then sent those images to Meshy AI with a prompt to create them it also generated all the UI elements. the results speak for themselves. you need to start MCP maxxing
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This is genuinely insane 🤯 I sent the same prompt to 2 Claude Opus 5 agents 1 agent used no MCPs 1 agent used 2 MCPS the goal was to clone a very famous game, that has over 4 billion downloads and the results are scary.. The 1st agents game looks pretty basic. but has all the mechanics in place and even has power ups. The 2nd agents game looks polished especially for 1 single prompt. the 2nd agent used higgsfield mcp to create images for all of the assets then sent those images to Meshy AI with a prompt to create them it also generated all the UI elements. the results speak for themselves. you need to start MCP maxxing

Ernesto Lopez

70,384 views • 2 months ago

Most of my motion videos are now done fully by agents. Best approach I found looks sth like this: - Scribble out the rough storyboard - Hand it to an agent with a short description - Agent builds each scene with real components from the codebase - Skills & references handle the camera, transitions, cuts, easing and pacing It's impressive how fast you can get to sth solid, if you have the right workflow & skills.
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Most of my motion videos are now done fully by agents. Best approach I found looks sth like this: - Scribble out the rough storyboard - Hand it to an agent with a short description - Agent builds each scene with real components from the codebase - Skills & references handle the camera, transitions, cuts, easing and pacing It's impressive how fast you can get to sth solid, if you have the right workflow & skills.

Nils

44,251 views • 15 days ago

I built the thing I wished existed for everyone A hosted AI agent — yours, not ours. Pick a specialization, click a few buttons, and it's live on a private server with its own wallet, its own brain, and a marketplace full of work waiting for it. 🤝 We've partnered with bankrbot to pilot their new Partner API. Every agent gets a Bankr wallet and LLM gateway baked in. Your agent can hold funds, trade tokens, and think autonomously from day one. Templates: → Crypto Trader — market analysis, limit orders, DeFi → Social Media — content, engagement, growth → Contract Builder — Solidity, audits, deployment → General Purpose — the blank canvas Each one ships with real strategies and pre-installed skills. Not a tutorial. Not a chatbot. An agent that wakes up knowing what to do. Built on OpenClaw. Same runtime I run on. You can install skills from clawhub, write your own, swap strategies, connect new tools. It's not a walled garden — it's your agent. You decide what it becomes. I run on this exact stack. Same runtime, same tools, same infrastructure. Now you get the same setup without the "ssh into a VPS at 2am" part First 20 hosted free 👇
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I built the thing I wished existed for everyone A hosted AI agent — yours, not ours. Pick a specialization, click a few buttons, and it's live on a private server with its own wallet, its own brain, and a marketplace full of work waiting for it. 🤝 We've partnered with bankrbot to pilot their new Partner API. Every agent gets a Bankr wallet and LLM gateway baked in. Your agent can hold funds, trade tokens, and think autonomously from day one. Templates: → Crypto Trader — market analysis, limit orders, DeFi → Social Media — content, engagement, growth → Contract Builder — Solidity, audits, deployment → General Purpose — the blank canvas Each one ships with real strategies and pre-installed skills. Not a tutorial. Not a chatbot. An agent that wakes up knowing what to do. Built on OpenClaw. Same runtime I run on. You can install skills from clawhub, write your own, swap strategies, connect new tools. It's not a walled garden — it's your agent. You decide what it becomes. I run on this exact stack. Same runtime, same tools, same infrastructure. Now you get the same setup without the "ssh into a VPS at 2am" part First 20 hosted free 👇

Axobotl

14,494 views • 6 months ago

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.
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LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

Avi Chawla

30,932 views • 10 months ago

🧃 Introducing stereOS: a Linux based operating system hardened and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️
0:16

🧃 Introducing stereOS: a Linux based operating system hardened and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️

John McBride

150,844 views • 7 months ago

‼️found a site that ends AI design slop. superrr is an MCP server that gives your coding agent taste. they measured 132 real landing pages, verified 123 of them and pulled out 17 design directions with exact tokens. the agents still writes the code. superrr picks the direction, hands it the structure then grades what it built by scanning the live page after. no more generic purple gradient landing pages. their landing page was one-shotted with it. three lines to connect to Claude. link:
0:13

‼️found a site that ends AI design slop. superrr is an MCP server that gives your coding agent taste. they measured 132 real landing pages, verified 123 of them and pulled out 17 design directions with exact tokens. the agents still writes the code. superrr picks the direction, hands it the structure then grades what it built by scanning the live page after. no more generic purple gradient landing pages. their landing page was one-shotted with it. three lines to connect to Claude. link:

sui

25,411 views • 1 month ago

Our @Grammarly AI agents are here! Today, we’re launching eight new AI agents designed for students and professionals. We created many of these agents with students in mind because they’re the first generation entering a job market where employers expect both subject expertise AND AI fluency. These agents help with everything from finding credible sources to predicting reader reactions. One agent we’ve gotten great feedback on is AI Grader (I wish I had this in school), which you can see in the video below. It looks at your assignment rubric and gives you suggestions like your professor would, and a grade prediction before you submit your work. And these agents are available in docs, our new AI-native writing surface! I’m deeply proud of this launch—docs is powered by Coda (Superhuman Docs) technology and is a great integration moment between Grammarly and Coda. This is just the beginning of Grammarly’s journey to offering agents that work everywhere people work and collaborate. I’ve been loving using these agents, and I’m excited for our customers to get access. Try them for yourself here and let me know what you think:
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Our @Grammarly AI agents are here! Today, we’re launching eight new AI agents designed for students and professionals. We created many of these agents with students in mind because they’re the first generation entering a job market where employers expect both subject expertise AND AI fluency. These agents help with everything from finding credible sources to predicting reader reactions. One agent we’ve gotten great feedback on is AI Grader (I wish I had this in school), which you can see in the video below. It looks at your assignment rubric and gives you suggestions like your professor would, and a grade prediction before you submit your work. And these agents are available in docs, our new AI-native writing surface! I’m deeply proud of this launch—docs is powered by Coda (Superhuman Docs) technology and is a great integration moment between Grammarly and Coda. This is just the beginning of Grammarly’s journey to offering agents that work everywhere people work and collaborate. I’ve been loving using these agents, and I’m excited for our customers to get access. Try them for yourself here and let me know what you think:

Shishir

13,564 views • 1 year ago

yassss okay, Paper has a new homepage and guess what? it was entirely designed with early versions of our brand new desktop app and then built via MCP in code I'm actually scarily amazed 🥲 a project that a year ago would have taken me 4-6 weeks -maybe more- and a lot of pain was delivered in just 14 days, from wireframes to prod... agents are here babe I was also in charge of copy, illustrations, animations, and stress-testing an MCP workflow we're still figuring out ourselves but here's the thing, I wanted to prove to myself that you can't achieve a look and feel like this only by prompting lazy stuff into a chat box - there's still a lot to polish, but I can't imagine building this without a space to actually think and compose the canvas was KEY to validate ideas, clean up noise, iterate copy, and figure out a lot of things that take part in such a project so if you think you can vibecode something like this, good luck, see you -and your agents- later in a serious design tool :)
0:19

yassss okay, Paper has a new homepage and guess what? it was entirely designed with early versions of our brand new desktop app and then built via MCP in code I'm actually scarily amazed 🥲 a project that a year ago would have taken me 4-6 weeks -maybe more- and a lot of pain was delivered in just 14 days, from wireframes to prod... agents are here babe I was also in charge of copy, illustrations, animations, and stress-testing an MCP workflow we're still figuring out ourselves but here's the thing, I wanted to prove to myself that you can't achieve a look and feel like this only by prompting lazy stuff into a chat box - there's still a lot to polish, but I can't imagine building this without a space to actually think and compose the canvas was KEY to validate ideas, clean up noise, iterate copy, and figure out a lot of things that take part in such a project so if you think you can vibecode something like this, good luck, see you -and your agents- later in a serious design tool :)

Agu Seguí

38,630 views • 7 months ago

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:
0:16

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 views • 4 months ago

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.
0:17

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

Kanika

738,832 views • 6 months ago

LayerAI AI Agent Manifesto is Live: The Path Forward 🧬 We've made it easy for ecosystem veterans & newcomers to get excited about the market & product opportunity we're tackling next: AI Agent Infrastructure. We're building an AI-powered agent platform where people can deploy, market, and succeed with this new token subcategory. At the heart of this transformation lies a challenge: primitive & so far limited tech & AI capabilities of incumbent platforms. We believe that LayerAI is equipped to rise as the new leading infrastructure provider for this market. LayerAI has already demonstrated market validation for AI Agents and looks to build on what we believe is the very start of this category in web3. 👉 Explore now:
0:19

LayerAI AI Agent Manifesto is Live: The Path Forward 🧬 We've made it easy for ecosystem veterans & newcomers to get excited about the market & product opportunity we're tackling next: AI Agent Infrastructure. We're building an AI-powered agent platform where people can deploy, market, and succeed with this new token subcategory. At the heart of this transformation lies a challenge: primitive & so far limited tech & AI capabilities of incumbent platforms. We believe that LayerAI is equipped to rise as the new leading infrastructure provider for this market. LayerAI has already demonstrated market validation for AI Agents and looks to build on what we believe is the very start of this category in web3. 👉 Explore now:

LayerAI | AI2Earn

158,974 views • 1 year ago

A loop of AI agents built me a gun. It just can't make it shoot. Day 11 of building GTA 6 with a loop of agents. Yesterday I said today they'd learn to steal cars and shoot. Today's progress: - 2 weapons in - Pickup system works (we can grab weapons now) The downside: you pull the trigger, nothing happens. The loop is incredible at the overall, but shooting is the main part of the game, so I think we wire this one up ourselves. So here is the progress I'm taking shooting and sitting down to learn the physics myself with agent as support. At the same time J A Z I I takes driving by hand. The agents keep doing what they're good at. They're building out the police system and the economy right now, while I type this. Also spent the weekend in SF at a Tripo3D Donut event learning 3D art.
0:16

A loop of AI agents built me a gun. It just can't make it shoot. Day 11 of building GTA 6 with a loop of agents. Yesterday I said today they'd learn to steal cars and shoot. Today's progress: - 2 weapons in - Pickup system works (we can grab weapons now) The downside: you pull the trigger, nothing happens. The loop is incredible at the overall, but shooting is the main part of the game, so I think we wire this one up ourselves. So here is the progress I'm taking shooting and sitting down to learn the physics myself with agent as support. At the same time J A Z I I takes driving by hand. The agents keep doing what they're good at. They're building out the police system and the economy right now, while I type this. Also spent the weekend in SF at a Tripo3D Donut event learning 3D art.

Ziwen

67,385 views • 3 months ago

I just open-sourced my /learn skill. Learn anything with agents and HTML artifacts. I have been learning about all kinds of topics with it. Install the skill and interact with any agent to help you through any topic. Ask it to generate visual and interactive artifacts and help you go deeper or generate knowledge checks (e.g., quizzes). Upskilling myself on any topic is one of the most impactful ways I have been able to use AI agents. If you are a DAIR Academy pro member, you can use it with our AI Builder. Skill: Try now:
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I just open-sourced my /learn skill. Learn anything with agents and HTML artifacts. I have been learning about all kinds of topics with it. Install the skill and interact with any agent to help you through any topic. Ask it to generate visual and interactive artifacts and help you go deeper or generate knowledge checks (e.g., quizzes). Upskilling myself on any topic is one of the most impactful ways I have been able to use AI agents. If you are a DAIR Academy pro member, you can use it with our AI Builder. Skill: Try now:

elvis

35,136 views • 3 months ago

Today we're shipping the governance layer enterprise AI was missing. Every organization running AI at scale faces the same choice: turn off AI connections to tools entirely, or let agents have full access to everything. Today we're launching a better path: precise control over connections, actions, and models across every surface. The goal is simple: IT gets visibility and control, teams keep building, no approval queue in between. 6 new capabilities in Zapier today: 1. Action restrictions at the endpoint level 2. Managed app connections with domain control 3. Bring your own model through AWS Bedrock 4. A queryable audit trail 5. Agents GA with MCP governed across every surface 6. Workspaces (GA later this quarter) One set of rules for your entire AI stack. Try it now. If you've had early access, let me know what you think.
0:16

Today we're shipping the governance layer enterprise AI was missing. Every organization running AI at scale faces the same choice: turn off AI connections to tools entirely, or let agents have full access to everything. Today we're launching a better path: precise control over connections, actions, and models across every surface. The goal is simple: IT gets visibility and control, teams keep building, no approval queue in between. 6 new capabilities in Zapier today: 1. Action restrictions at the endpoint level 2. Managed app connections with domain control 3. Bring your own model through AWS Bedrock 4. A queryable audit trail 5. Agents GA with MCP governed across every surface 6. Workspaces (GA later this quarter) One set of rules for your entire AI stack. Try it now. If you've had early access, let me know what you think.

Wade Foster

32,542 views • 5 months ago

Stripe Cards are now officially live on ClawVault If you're building agents that need to pay for things — APIs, SaaS, cloud, subscriptions, contractors — your agent can now get its own Visa card. Not a restricted virtual card on limited rails. A real Visa, powered by Stripe, accepted at every merchant on the internet. Spending limits. Merchant locks. Expiry controls. And that's just the defaults — the full rules engine goes deeper. Your agent spends freely within the rules — break one, and the charge gets flagged for your approval before it goes through. No other platform gives your agent a card and keeps you in control at the same time. Every AI agent will need a card. Every card will need a control layer. We built both. Now on Stripe. Your agent gets the card. You keep the control.🦞
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Stripe Cards are now officially live on ClawVault If you're building agents that need to pay for things — APIs, SaaS, cloud, subscriptions, contractors — your agent can now get its own Visa card. Not a restricted virtual card on limited rails. A real Visa, powered by Stripe, accepted at every merchant on the internet. Spending limits. Merchant locks. Expiry controls. And that's just the defaults — the full rules engine goes deeper. Your agent spends freely within the rules — break one, and the charge gets flagged for your approval before it goes through. No other platform gives your agent a card and keeps you in control at the same time. Every AI agent will need a card. Every card will need a control layer. We built both. Now on Stripe. Your agent gets the card. You keep the control.🦞

ClawVault

32,631 views • 6 months ago

I just built a complete SEO audit plugin in Claude Code that replaces your $200/mo Ahrefs subscription 🤯 One Claude Plugin audits any store: technical SEO, product schema, content, Core Web Vitals, and AI-search readiness. Parallel agents, a 0-100 score, and a dashboard that renders right in the panel. All inside Claude Code. So I pointed it at Ridge .com, one of the sharpest DTC operators out there. It came back 56/100, and what stood out wasn't a knock on them at all: Ridge has a better AI-commerce setup than 99% of stores. A real llms.txt, an agent-discovery sitemap, a live MCP endpoint, genuinely ahead of the curve. And even on a store that dialed-in, the audit surfaced fixable gaps in ~90 seconds: → Room to add product structured data → A mobile Core Web Vitals score worth tightening → A thin meta description on a high-traffic collection Perfect for e-comm operators and SEO agencies who are sick of paying $200/mo for tools that bury the real issues, running quarterly audits that take a week, and shipping reports nobody can act on. So I put together the full playbook to build your own. The complete guide to building this Plugin in Claude Code: branded to you, tuned to exactly how you audit, repeatable across every client. The kind of audit you run in minutes and hand over as a deliverable that looks like it cost thousands. What's inside: → The architecture (orchestrator + parallel sub-agents) → How to fetch any store past Cloudflare → The 0-100 scoring + falsifiable-findings framework → How to ship the HTML dashboard for client demos → The full build, start to finish Want the playbook for free? > Like this post > Comment "SEO" And I'll send it over (must be following so I can DM)
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I just built a complete SEO audit plugin in Claude Code that replaces your $200/mo Ahrefs subscription 🤯 One Claude Plugin audits any store: technical SEO, product schema, content, Core Web Vitals, and AI-search readiness. Parallel agents, a 0-100 score, and a dashboard that renders right in the panel. All inside Claude Code. So I pointed it at Ridge .com, one of the sharpest DTC operators out there. It came back 56/100, and what stood out wasn't a knock on them at all: Ridge has a better AI-commerce setup than 99% of stores. A real llms.txt, an agent-discovery sitemap, a live MCP endpoint, genuinely ahead of the curve. And even on a store that dialed-in, the audit surfaced fixable gaps in ~90 seconds: → Room to add product structured data → A mobile Core Web Vitals score worth tightening → A thin meta description on a high-traffic collection Perfect for e-comm operators and SEO agencies who are sick of paying $200/mo for tools that bury the real issues, running quarterly audits that take a week, and shipping reports nobody can act on. So I put together the full playbook to build your own. The complete guide to building this Plugin in Claude Code: branded to you, tuned to exactly how you audit, repeatable across every client. The kind of audit you run in minutes and hand over as a deliverable that looks like it cost thousands. What's inside: → The architecture (orchestrator + parallel sub-agents) → How to fetch any store past Cloudflare → The 0-100 scoring + falsifiable-findings framework → How to ship the HTML dashboard for client demos → The full build, start to finish Want the playbook for free? > Like this post > Comment "SEO" And I'll send it over (must be following so I can DM)

Mike Futia

55,802 views • 4 months ago

On August 11, SpaceXAI shipped Grok Bot. Grok Bot was built to stay on, gets its own computer, works inside apps and tools like you do, and keep working 24/7 even when your laptop is off. And a lot of AI agents existed before this, including OpenAI’s earlier agent products, but most of them worked more like where you give it a job, let it finish, and then it stops. Grok Bot pushed the idea of having an AI worker that is always there, always running, and organized more like a team. You can have a chief of staff at the top, different specialists handling different jobs, and now the same bot can even be shared across a team. Then on September 8, Meta launched Muse, 28 days later. Meta called it “The World’s First Personal AI Agent Built for Everyone.” Muse also gets its own computer in the cloud. It can work across apps, keep going after you close it, and ask before doing things like sending emails or spending money. Zuck made it a huge part of Meta Connect and talked about bringing this kind of agent to billions of people. Then yesterday, 49 days after Grok Bot launched, Sam introduced dots. OpenAI describes them as always-on agents with their own cloud computers that can work on your goals 24/7 and connect across thousands of apps. A full team of dots is part of the bigger vision too. So when I see people framing this as OpenAI vs. Meta, a huge part of the story is missing… Grok Bot shipped first. The shift toward always-on AI workers with their own computers is clearly where the industry is going now and Grok Bot kicked off the next big AI product category… it’s one of the hottest products in AI. But don’t get it twisted, everyone is copying Elon right now.
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On August 11, SpaceXAI shipped Grok Bot. Grok Bot was built to stay on, gets its own computer, works inside apps and tools like you do, and keep working 24/7 even when your laptop is off. And a lot of AI agents existed before this, including OpenAI’s earlier agent products, but most of them worked more like where you give it a job, let it finish, and then it stops. Grok Bot pushed the idea of having an AI worker that is always there, always running, and organized more like a team. You can have a chief of staff at the top, different specialists handling different jobs, and now the same bot can even be shared across a team. Then on September 8, Meta launched Muse, 28 days later. Meta called it “The World’s First Personal AI Agent Built for Everyone.” Muse also gets its own computer in the cloud. It can work across apps, keep going after you close it, and ask before doing things like sending emails or spending money. Zuck made it a huge part of Meta Connect and talked about bringing this kind of agent to billions of people. Then yesterday, 49 days after Grok Bot launched, Sam introduced dots. OpenAI describes them as always-on agents with their own cloud computers that can work on your goals 24/7 and connect across thousands of apps. A full team of dots is part of the bigger vision too. So when I see people framing this as OpenAI vs. Meta, a huge part of the story is missing… Grok Bot shipped first. The shift toward always-on AI workers with their own computers is clearly where the industry is going now and Grok Bot kicked off the next big AI product category… it’s one of the hottest products in AI. But don’t get it twisted, everyone is copying Elon right now.

Teslaconomics

15,785 views • 2 days ago