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Ana Sayfaya Dön

This might be the best agent I've seen yet. After raising $220M, H just introduced an agent that can execute any task from a prompt. Their "Runner H" can basically turn instructions into action with human-like precision. Features: ▸ Navigates web interfaces with pixel-level precision. ▸ Interprets pixels and... show more

Lior Alexander

116,701 subscribers

106,696 görüntüleme • 1 yıl önce •via X (Twitter)

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Lior⚡ profil fotoğrafı

Lior⚡1 yıl önce

You san signup for the private beta here:

An Qu profil fotoğrafı

An Qu1 yıl önce

@hcompany_ai If you don't have $220M, here's my free web agent chrome extension. It can execute any* task from a prompt too:

nick profil fotoğrafı

nick1 yıl önce

@hcompany_ai Hmmm not too hard to imagine it accidentally spending all your money and leaking your banking info

John E-gen 🔮 profil fotoğrafı

John E-gen 🔮1 yıl önce

for $220M y’all would think someone could offer a defense from anyone being able to hack it into dust there is no technology that can stop probabilistic injections if you silo the tech it is safe and secure and magical if you face the customer , you can/will be a liability target

Shaun Prince profil fotoğrafı

Shaun Prince1 yıl önce

@Trillion0x @hcompany_ai I wonder how soon we will find an open source version that runs locally, using local llms. There is no future for starting a business and locking people behind a subscription. Plenty of projects that already come very close.

Burny — Effective Omni profil fotoğrafı

Burny — Effective Omni1 yıl önce

@hcompany_ai AI job applying is technologically cool but I fear this will kill online job applications because the signal will drown in noise and people will return to applying only in person

Joseph Pitluck Aguirre profil fotoğrafı

Joseph Pitluck Aguirre1 yıl önce

@hcompany_ai Brother, $220M and only a 67% success rate on WebVoyager?

martin.p profil fotoğrafı

martin.p1 yıl önce

@hcompany_ai do you know how this differs from claude's Use Computer feature?

Engineer Investor profil fotoğrafı

Engineer Investor1 yıl önce

@hcompany_ai It is so small,2B-3B, do this means that it is opensource?

☠️ Reaper ☠️ Capital ☠️ profil fotoğrafı

☠️ Reaper ☠️ Capital ☠️1 yıl önce

@hcompany_ai I have applied @hcompany_ai 🙏

Benzer Videolar

This might be the most advanced AI agent to date. It’s called Runner H by H, and it automates complex, multi-step tasks from just a prompt. You basically describe what you want and it takes over your computer to complete it. Some key features: - Outperforms competitors like Anthropic’s Computer Use on the WebVoyager benchmark. - Navigates web interfaces through pixel-level interpretation and semantic understanding. - Adapts to UI changes automatically. - Handles complex workflows like e-commerce and financial services onboarding. They have already raised $220M and looks like we are entering the agentic era! You can sign up to their waitlist now (link below).

This might be the most advanced AI agent to date. It’s called Runner H by H, and it automates complex, multi-step tasks from just a prompt. You basically describe what you want and it takes over your computer to complete it. Some key features: - Outperforms competitors like Anthropic’s Computer Use on the WebVoyager benchmark. - Navigates web interfaces through pixel-level interpretation and semantic understanding. - Adapts to UI changes automatically. - Handles complex workflows like e-commerce and financial services onboarding. They have already raised $220M and looks like we are entering the agentic era! You can sign up to their waitlist now (link below).

Alvaro Cintas

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Introducing the Studio, our platform for developers to run automations at scale, and Runner H, the most advanced agent to date — designed to navigate web interfaces through pixel-level interpretation and semantic understanding. You can now turn your instructions into action with human-like precision.

Introducing the Studio, our platform for developers to run automations at scale, and Runner H, the most advanced agent to date — designed to navigate web interfaces through pixel-level interpretation and semantic understanding. You can now turn your instructions into action with human-like precision.

H

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AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

Santiago

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Andrew Ng

186,133 görüntüleme • 1 yıl önce

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Chatbase

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Andrew Ng

110,606 görüntüleme • 2 yıl önce

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Andrew Ng

63,773 görüntüleme • 7 ay önce

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Francesco

30,834 görüntüleme • 2 yıl önce

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OptimAI Network

18,524 görüntüleme • 2 ay önce

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Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:

Andrew Ng

118,063 görüntüleme • 9 ay önce

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

Santiago

46,109 görüntüleme • 1 yıl önce

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

AI at Meta

2,265,314 görüntüleme • 1 yıl önce

AutoGPT might be the next big step in AI. Here's why Karpathy recently said "AutoGPT is the next frontier of prompt engineering" AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and have it autonomously come up with a plan, execute on it, browse the web, and use new data to revise the strategy until the task is completed. It can analyze the market and come up with a trading strategy, customer service, marketing, finance, or other tasks that requires continuous updates. There are three components to it: 1. Architecture: It leverages GPT-4 and GPT-3.5 via API. 2. Autonomous Iterations: AutoGPT can refine its outputs by self-critical review, building on its previous work and integrating prompt history for more accurate results. 3. Memory Management: Integration with Pinecone allows for long-term memory storage, enabling context preservation and improved decision-making. 4. Multi-functionality: Capabilities include file manipulation, web browsing, and data retrieval, distinguishing AutoGPT from previous AI advancements by broadening its application scope.

AutoGPT might be the next big step in AI. Here's why Karpathy recently said "AutoGPT is the next frontier of prompt engineering" AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and have it autonomously come up with a plan, execute on it, browse the web, and use new data to revise the strategy until the task is completed. It can analyze the market and come up with a trading strategy, customer service, marketing, finance, or other tasks that requires continuous updates. There are three components to it: 1. Architecture: It leverages GPT-4 and GPT-3.5 via API. 2. Autonomous Iterations: AutoGPT can refine its outputs by self-critical review, building on its previous work and integrating prompt history for more accurate results. 3. Memory Management: Integration with Pinecone allows for long-term memory storage, enabling context preservation and improved decision-making. 4. Multi-functionality: Capabilities include file manipulation, web browsing, and data retrieval, distinguishing AutoGPT from previous AI advancements by broadening its application scope.

Lior Alexander

808,102 görüntüleme • 3 yıl önce

The #1 problem with coding agents right now: Ask them to solve one problem, and they will make 10 other changes you didn't want. This happens to me every day. It happens to everyone I talk to as well. We have a solution for this now. The team Augment Code released a "Task List" feature for their coding assistant that solves this problem. Augment Code is partnering with me on this post. In case you haven't used them before: • Augment Code is a fully-fledged coding assistant • Their specialty are large projects • Fastest coding indexing I've seen • Has a free forever community edition Now, you can ask their coding agent to generate a Task List before doing anything. This will give you a plan you can review, edit, and augment if you need to. You can export this plan, load it on a different session, or even share it across projects. It makes a huge difference: The task list constrains the agent so you won't get any "unintended" changes anymore. It also puts you in control of everything the agent does. Check the video to see the agent working through a task list. You can also try this 100% free: (By the way, they also have support for remote agents. You can basically have those agents write your code while you are sleeping.)

The #1 problem with coding agents right now: Ask them to solve one problem, and they will make 10 other changes you didn't want. This happens to me every day. It happens to everyone I talk to as well. We have a solution for this now. The team Augment Code released a "Task List" feature for their coding assistant that solves this problem. Augment Code is partnering with me on this post. In case you haven't used them before: • Augment Code is a fully-fledged coding assistant • Their specialty are large projects • Fastest coding indexing I've seen • Has a free forever community edition Now, you can ask their coding agent to generate a Task List before doing anything. This will give you a plan you can review, edit, and augment if you need to. You can export this plan, load it on a different session, or even share it across projects. It makes a huge difference: The task list constrains the agent so you won't get any "unintended" changes anymore. It also puts you in control of everything the agent does. Check the video to see the agent working through a task list. You can also try this 100% free: (By the way, they also have support for remote agents. You can basically have those agents write your code while you are sleeping.)

Santiago

41,738 görüntüleme • 1 yıl önce

"Orgs and cos building MCP servers are taking an LLM-first approach to what the API needs to expose to the agent(s)." - Nikunj Handa from OpenAI "For example, Stripe has a bunch of APIs that can be used to create a subscription/customer/product/price. For an LLM, it can just combine that into a single function." "Instead of returning this massive JSON object, they can return something very specific to the task being solved, so that the LLM can more easily understand what's happening." "It's an opportunity to rewrite your APIs to be very LLM-first. Why do 2 hours of work, when you can do it in 4 lines of code under a minute?"

"Orgs and cos building MCP servers are taking an LLM-first approach to what the API needs to expose to the agent(s)." - Nikunj Handa from OpenAI "For example, Stripe has a bunch of APIs that can be used to create a subscription/customer/product/price. For an LLM, it can just combine that into a single function." "Instead of returning this massive JSON object, they can return something very specific to the task being solved, so that the LLM can more easily understand what's happening." "It's an opportunity to rewrite your APIs to be very LLM-first. Why do 2 hours of work, when you can do it in 4 lines of code under a minute?"

TBPN

11,445 görüntüleme • 1 yıl önce

🚨 Breaking News OpenAI just announced the launch of its groundbreaking ChatGPT agent. It can navigate calendars, generate editable presentations, execute code, and more. ⁠ What ChatGPT Agent Can Do: Task Automation: Easily manage your calendar, craft professional presentations and slideshows, and run code automatically. Deep Web Integration: Combines functionalities from OpenAI’s earlier tools like Operator (for web interactions) and Deep Research (for synthesizing extensive web-based data into clear reports). Natural Interaction: Simply prompt ChatGPT using natural language to get tasks done. App Connectivity: Supports connectors to popular apps like Gmail and GitHub, and can directly use APIs to extend functionality even further. ⁠ 📈 Impressive Benchmarks: Humanity’s Last Exam: Scores 41.6%, nearly double the previous models (OpenAI o3 and o4-mini). FrontierMath: Achieves a remarkable 27.4% with tool assistance, significantly surpassing previous leading scores (6.3% from o4-mini). ⁠ Built with Safety in Mind: OpenAI emphasizes safety due to the agent’s enhanced capabilities, ensuring responsible usage while exploring new frontiers of AI interactions. ⁠ Now available for ChatGPT Pro, Plus, and Team subscribers. Just activate “agent mode” from the ChatGPT menu and explore a smarter, more productive future!

🚨 Breaking News OpenAI just announced the launch of its groundbreaking ChatGPT agent. It can navigate calendars, generate editable presentations, execute code, and more. ⁠ What ChatGPT Agent Can Do: Task Automation: Easily manage your calendar, craft professional presentations and slideshows, and run code automatically. Deep Web Integration: Combines functionalities from OpenAI’s earlier tools like Operator (for web interactions) and Deep Research (for synthesizing extensive web-based data into clear reports). Natural Interaction: Simply prompt ChatGPT using natural language to get tasks done. App Connectivity: Supports connectors to popular apps like Gmail and GitHub, and can directly use APIs to extend functionality even further. ⁠ 📈 Impressive Benchmarks: Humanity’s Last Exam: Scores 41.6%, nearly double the previous models (OpenAI o3 and o4-mini). FrontierMath: Achieves a remarkable 27.4% with tool assistance, significantly surpassing previous leading scores (6.3% from o4-mini). ⁠ Built with Safety in Mind: OpenAI emphasizes safety due to the agent’s enhanced capabilities, ensuring responsible usage while exploring new frontiers of AI interactions. ⁠ Now available for ChatGPT Pro, Plus, and Team subscribers. Just activate “agent mode” from the ChatGPT menu and explore a smarter, more productive future!

There's An AI For That

32,716 görüntüleme • 1 yıl önce

This one is for builders who want an agent that can operate their computer. Today, we're releasing HoloDesktop CLI. Powered by Holo3 models, it brings H Agent directly into the agent harnesses you already use, including Claude R Perrin Code, Hermes Trismegistus, Cursor, and others. It runs locally on your device with low latency, full privacy, and no runtime cost. Most AI tooling assumes work happens through APIs. In reality, much of the world's work still happens in interfaces built for humans: browsers, desktop applications, spreadsheets, internal tools, and software with no API. HoloDesktop CLI gives agents the ability to see, understand, and act on these environments through the mouse and keyboard. From testing web applications through their GUI to navigating enterprise software, extracting information from documents, or interacting with internal tools, agents can now operate where work actually happens. We believe AI shouldn't just reason about work. It should be able to operate where work happens. #NVIDIA #ComputerUse #EnterpriseAI #DeveloperTools

This one is for builders who want an agent that can operate their computer. Today, we're releasing HoloDesktop CLI. Powered by Holo3 models, it brings H Agent directly into the agent harnesses you already use, including Claude R Perrin Code, Hermes Trismegistus, Cursor, and others. It runs locally on your device with low latency, full privacy, and no runtime cost. Most AI tooling assumes work happens through APIs. In reality, much of the world's work still happens in interfaces built for humans: browsers, desktop applications, spreadsheets, internal tools, and software with no API. HoloDesktop CLI gives agents the ability to see, understand, and act on these environments through the mouse and keyboard. From testing web applications through their GUI to navigating enterprise software, extracting information from documents, or interacting with internal tools, agents can now operate where work actually happens. We believe AI shouldn't just reason about work. It should be able to operate where work happens. #NVIDIA #ComputerUse #EnterpriseAI #DeveloperTools

H

29,953 görüntüleme • 1 ay önce

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:

Andrew Ng

126,478 görüntüleme • 1 yıl önce

Hands are as important as the brain. From Shenzhen, Stella Robot has unveiled the PANTHEON HAND 22:a new generation of bionic dexterous hand that combines 22 degrees of freedom with remarkable strength and finesse. It’s a hand that truly understands precision. It can lift 30 kilograms, yet is delicate enough to thread a needle.Each finger can bear 1.5 kilograms, and the entire hand can gently hold a piece of tofu or sense the subtle expansion of a balloon. Powered by a tendon-driven mechanism with integrated actuators inside the forearm, it achieves compact design and natural, human-like movement. From precision assembly and component handling to material transport and human-robot collaboration,the PANTHEON HAND 22 shows what it means for humanoid robots to truly work with their hands.

Hands are as important as the brain. From Shenzhen, Stella Robot has unveiled the PANTHEON HAND 22:a new generation of bionic dexterous hand that combines 22 degrees of freedom with remarkable strength and finesse. It’s a hand that truly understands precision. It can lift 30 kilograms, yet is delicate enough to thread a needle.Each finger can bear 1.5 kilograms, and the entire hand can gently hold a piece of tofu or sense the subtle expansion of a balloon. Powered by a tendon-driven mechanism with integrated actuators inside the forearm, it achieves compact design and natural, human-like movement. From precision assembly and component handling to material transport and human-robot collaboration,the PANTHEON HAND 22 shows what it means for humanoid robots to truly work with their hands.

CyberRobo

26,867 görüntüleme • 9 ay önce