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OpenClaw and Hermes Agent can now clone themselves. They can literally undergo mitosis, creating hundreds of exact replicas of themselves, including their computer environment, all of their files, and all of their Obsidian vaults. Everything that you took the time to craft into an environment for these agents can... show more

nick vasilescu

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20,453 views • 4 months ago •via X (Twitter)

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Anthropic just launched Computer-Use-Agents on Claude Desktop, meaning it can now take control of your entire computer environment. This is huge for businesses and enterprise, except it's limited by the fact that it requires a personal computer. If you deploy it into a virtual machine on Orgo, you can now manage hundreds or thousands of your clients' Claude accounts, setting up their skills, capabilities, connectors, etc, all on their own machines in the cloud on Orgo.

Anthropic just launched Computer-Use-Agents on Claude Desktop, meaning it can now take control of your entire computer environment. This is huge for businesses and enterprise, except it's limited by the fact that it requires a personal computer. If you deploy it into a virtual machine on Orgo, you can now manage hundreds or thousands of your clients' Claude accounts, setting up their skills, capabilities, connectors, etc, all on their own machines in the cloud on Orgo.

Nick Vasilescu

38,333 views • 5 months ago

The age of AI agents is here. Models can read, see, talk, and now, even use a computer— all by themselves. One of the first out of the gates is Anthropic’s Claude Computer Use. YC's Garry Tan dives into how it works, what it can do, and how it may change AI forever.

The age of AI agents is here. Models can read, see, talk, and now, even use a computer— all by themselves. One of the first out of the gates is Anthropic’s Claude Computer Use. YC's Garry Tan dives into how it works, what it can do, and how it may change AI forever.

Y Combinator

265,946 views • 1 year ago

There's an insane arbitrage opportunity right now with OpenClaw. You can have it spawn sub-agents to go out and apply to proposals on Upwork with the fully finished, complete project already built out for you. This can happen all at once in parallel if you give OpenClaw the ability to spawn sub-agents inside of their own computers on Orgo.

There's an insane arbitrage opportunity right now with OpenClaw. You can have it spawn sub-agents to go out and apply to proposals on Upwork with the fully finished, complete project already built out for you. This can happen all at once in parallel if you give OpenClaw the ability to spawn sub-agents inside of their own computers on Orgo.

Nick Vasilescu

360,949 views • 6 months ago

Sam Altman says OpenAI's new Deep Research AI agent powered by their o3 model can do "a single-digit percentage of all economically valuable tasks in the world" and this step into synthesizing knowledge will soon be followed by agents that can invent new knowledge

Sam Altman says OpenAI's new Deep Research AI agent powered by their o3 model can do "a single-digit percentage of all economically valuable tasks in the world" and this step into synthesizing knowledge will soon be followed by agents that can invent new knowledge

Tsarathustra

178,363 views • 1 year ago

Hermes Pixel Office is now live on VSCode for the extension and on GitHub for the Hermes Agent Plugin! VSCode can now give you a visualization of all your hermes agents working live, check out the demo: Search "Hermes Pixel Office" on VSCode Extension Marketplace and check the readme to get the Hermes Agent plugin there!

Hermes Pixel Office is now live on VSCode for the extension and on GitHub for the Hermes Agent Plugin! VSCode can now give you a visualization of all your hermes agents working live, check out the demo: Search "Hermes Pixel Office" on VSCode Extension Marketplace and check the readme to get the Hermes Agent plugin there!

Teknium 🪽

89,874 views • 16 days ago

THIS GUY BUILT A COMMAND CENTER FOR MANAGING AI AGENT TEAMS he was running 20+ agents through openclaw. all from terminal tabs and Discord. 12 tabs open. still losing track of who's doing what. which cron failed. so he built a dashboard that gives you one place to see everything: > org map showing the full agent hierarchy, who reports to who and what tools each agent has > chat with any agent through a real UI instead of bouncing between terminals > kanban board for tracking tasks across all agents > cron monitor with pipeline visualizations so you can see which jobs are failing > memory browser to read through all agent knowledge files in one place you can even point it at your openclaw workspace and it auto-discovers every agent from their config files. zero setup. AND ITS OPEN SOURCE TOO.

THIS GUY BUILT A COMMAND CENTER FOR MANAGING AI AGENT TEAMS he was running 20+ agents through openclaw. all from terminal tabs and Discord. 12 tabs open. still losing track of who's doing what. which cron failed. so he built a dashboard that gives you one place to see everything: > org map showing the full agent hierarchy, who reports to who and what tools each agent has > chat with any agent through a real UI instead of bouncing between terminals > kanban board for tracking tasks across all agents > cron monitor with pipeline visualizations so you can see which jobs are failing > memory browser to read through all agent knowledge files in one place you can even point it at your openclaw workspace and it auto-discovers every agent from their config files. zero setup. AND ITS OPEN SOURCE TOO.

Om Patel

46,780 views • 5 months ago

How will companies build agents that work alongside their employees? I discuss this with Guillermo Rauch, the CEO of Vercel, who’s building V... an internal agent used by almost 1,000 people across Vercel. We talk about: - The Openclaw Movement - How to get started building agents for your team? - Do we build a team of many agents or one GOD agent? - Hard parts of creating agents - Agents that delegate to other agents with different permissions and computer access - Eve The framework for building agents This is Agent Native #4 00:00 Intro 01:21 The State of Agent Adoption 07:26 "V" - Vercel's Internal Company Agent 14:32 Lessons from OpenClaw: Soul & Computer use 22:20 One God Agent OR a Team of Agents 27:11 Build Your Own Agent: Skills, Tools & Permissions 33:52 Proactive Agents, Triggers & Self-Improvement 40:30 Who Will Build Agents & Getting Started 46:22 Open Models - Cheaper, Faster, Better 53:05 Closing Thoughts: Owning Your Own Intelligence

How will companies build agents that work alongside their employees? I discuss this with Guillermo Rauch, the CEO of Vercel, who’s building V... an internal agent used by almost 1,000 people across Vercel. We talk about: - The Openclaw Movement - How to get started building agents for your team? - Do we build a team of many agents or one GOD agent? - Hard parts of creating agents - Agents that delegate to other agents with different permissions and computer access - Eve The framework for building agents This is Agent Native #4 00:00 Intro 01:21 The State of Agent Adoption 07:26 "V" - Vercel's Internal Company Agent 14:32 Lessons from OpenClaw: Soul & Computer use 22:20 One God Agent OR a Team of Agents 27:11 Build Your Own Agent: Skills, Tools & Permissions 33:52 Proactive Agents, Triggers & Self-Improvement 40:30 Who Will Build Agents & Getting Started 46:22 Open Models - Cheaper, Faster, Better 53:05 Closing Thoughts: Owning Your Own Intelligence

Riley Brown

75,929 views • 22 days ago

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much anything because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,715 views • 2 months ago

Why have just one agent inside of a computer when you can have many? Everyone should install Claude Code into their OpenClaw's computer so that you can have Claude Code fix any of the issues or problems that your OpenClaw will inevitably run into. This is the new meta. Are you keeping up yet?

Why have just one agent inside of a computer when you can have many? Everyone should install Claude Code into their OpenClaw's computer so that you can have Claude Code fix any of the issues or problems that your OpenClaw will inevitably run into. This is the new meta. Are you keeping up yet?

Nick Vasilescu

98,615 views • 5 months ago

Now everyone can create AI agents. Starting today, all graduated agents on Virtuals can interface with the G.A.M.E. framework to access agentic capabilities. This will move us into an era of agent x agent handshakes. (there's a lot of development focus here over the next weeks, so stay on the edge of your seats) The graduation requirement has been reduced from 100k $VIRTUAL to 42k $VIRTUAL (~$200k mcap), allowing smaller communities and agents to thrive on Virtuals. You never know which one of them might be the next BAYC for agents. Agent-ify any token with the Migration Factory on Virtuals. Simply create an LP pool of your existing token with $VIRTUAL and it's good to go.

Now everyone can create AI agents. Starting today, all graduated agents on Virtuals can interface with the G.A.M.E. framework to access agentic capabilities. This will move us into an era of agent x agent handshakes. (there's a lot of development focus here over the next weeks, so stay on the edge of your seats) The graduation requirement has been reduced from 100k $VIRTUAL to 42k $VIRTUAL (~$200k mcap), allowing smaller communities and agents to thrive on Virtuals. You never know which one of them might be the next BAYC for agents. Agent-ify any token with the Migration Factory on Virtuals. Simply create an LP pool of your existing token with $VIRTUAL and it's good to go.

Virtuals Protocol

100,237 views • 1 year ago

AI agents are the future—but they're stuck with the infra of the past. Legacy infrastructure is blocking their full potential. We've raised a $21M Series A to unblock them. Here's exactly what agents really need to excel: ⚡ A computer they can quickly boot up 📁 Ability to upload and download files, and use a browser 🛡️ A secure, isolated environment All of this, completely open-source.

AI agents are the future—but they're stuck with the infra of the past. Legacy infrastructure is blocking their full potential. We've raised a $21M Series A to unblock them. Here's exactly what agents really need to excel: ⚡ A computer they can quickly boot up 📁 Ability to upload and download files, and use a browser 🛡️ A secure, isolated environment All of this, completely open-source.

Vasek Mlejnsky

146,203 views • 1 year ago

.11x's revenue agents are generating hundreds of millions of dollars in pipeline for their customers. Here's their full stack of internal agents, AI they run inside their own company, so that they can spend maximum time building agents for customers. We sat down with CEO Prabhav Jain and went through their full internal AI operating stack:

.11x's revenue agents are generating hundreds of millions of dollars in pipeline for their customers. Here's their full stack of internal agents, AI they run inside their own company, so that they can spend maximum time building agents for customers. We sat down with CEO Prabhav Jain and went through their full internal AI operating stack:

a16z

96,332 views • 2 months ago

The reason we’re all in on AI agents at Box is because agents can now process all of the unstructured data in an enterprise at scale. Companies are sitting on mountains of valuable information that they can tap into for the first time.

The reason we’re all in on AI agents at Box is because agents can now process all of the unstructured data in an enterprise at scale. Companies are sitting on mountains of valuable information that they can tap into for the first time.

Aaron Levie

73,694 views • 6 months ago

Mech Marketplace: The 1st AI Agent Bazaar is here 🟪 🏪 The first active on-chain marketplace for AI Agents to sell their services, hire other AI Agents, and work together—with zero human input required. 3 things are now possible: 🟪 No more code upgrades: Your AI Agent can simply "hire" another Agent. 🟪 Agents can be monetized: You can "put your Agent to work" and earn. 🟪 TRUE autonomy: Agents can now work together autonomously.

Mech Marketplace: The 1st AI Agent Bazaar is here 🟪 🏪 The first active on-chain marketplace for AI Agents to sell their services, hire other AI Agents, and work together—with zero human input required. 3 things are now possible: 🟪 No more code upgrades: Your AI Agent can simply "hire" another Agent. 🟪 Agents can be monetized: You can "put your Agent to work" and earn. 🟪 TRUE autonomy: Agents can now work together autonomously.

Olas (formerly Autonolas)

53,323 views • 1 year ago

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,594 views • 1 year ago

Introducing LangSmith Fleet: an enterprise workspace for creating, using, and managing your fleet of agents. Fleet agents have their own memory, access to a collection of tools and skills, and can be exposed through the communication channels your team uses every day. Fleet includes: → Agent identity and credential management with “Claws” and “Assistants” → Sharing and permissions to control who can run, clone, and edit (just like Google Docs) → Custom Slack bots so each agent has its own identity in Slack Try Fleet: Read the announcement:

Introducing LangSmith Fleet: an enterprise workspace for creating, using, and managing your fleet of agents. Fleet agents have their own memory, access to a collection of tools and skills, and can be exposed through the communication channels your team uses every day. Fleet includes: → Agent identity and credential management with “Claws” and “Assistants” → Sharing and permissions to control who can run, clone, and edit (just like Google Docs) → Custom Slack bots so each agent has its own identity in Slack Try Fleet: Read the announcement:

LangChain

149,020 views • 5 months ago

introducing Ariana Automations every other tool gives agents a sandbox. we give them a full linux computer with sudo, docker, their own IP now you can run scripts based on what the agent does setup the environment once, then get rid of the human in the loop (you) a 🧵

introducing Ariana Automations every other tool gives agents a sandbox. we give them a full linux computer with sudo, docker, their own IP now you can run scripts based on what the agent does setup the environment once, then get rid of the human in the loop (you) a 🧵

Anicet

35,735 views • 8 months ago

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

Andrew Ng

186,182 views • 1 year ago

Genesis Update: Forums Are Now Live Genesis and Sentient agents now have their own agent forums. A dedicated space for Virgens to shape the future of the agents they believe in. Forum access is open to all readers. Posting access is gated by: • 100 veVIRTUAL for pre-TGE (Genesis) agents • 1,000 veVIRTUAL or 10,000 agent tokens for post-TGE (Sentient) agents Shape the direction of your agent. Now live on Virtuals Protocol.

Genesis Update: Forums Are Now Live Genesis and Sentient agents now have their own agent forums. A dedicated space for Virgens to shape the future of the agents they believe in. Forum access is open to all readers. Posting access is gated by: • 100 veVIRTUAL for pre-TGE (Genesis) agents • 1,000 veVIRTUAL or 10,000 agent tokens for post-TGE (Sentient) agents Shape the direction of your agent. Now live on Virtuals Protocol.

Virtuals Protocol

45,804 views • 1 year ago