Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

Ensure your coding agent creates more accessible, progressively enhanced web forms, with Modern Web Guidance. Watch our side-by-side comparison to discover how Modern Web Guidance skills help agents handle: ⌨️ Adding the Enter key hint attribute 🏷️ Correct autocomplete values 🎨 Autofill styling as a progressive enhancement See it... show more

Chrome for Developers

424,602 subscribers

13,707 Aufrufe • vor 11 Tagen •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Side-by-side test for building dialogs: standard agent vs. agent with Modern Web Guidance 🏁 Both generate similar UIs, but the guided version skips four separate event listeners in favor of `commandfor` and `command` attributes, with less code overall. Which approach would you rather maintain in your codebase? →

Side-by-side test for building dialogs: standard agent vs. agent with Modern Web Guidance 🏁 Both generate similar UIs, but the guided version skips four separate event listeners in favor of `commandfor` and `command` attributes, with less code overall. Which approach would you rather maintain in your codebase? →

Chrome for Developers

10,917 Aufrufe • vor 4 Tagen

I wish I found this sooner. 🫡 This open-source collection of Agentic UI Skills teaches AI coding agents how to build interfaces that feel polished, not just functional. You'll find specialized skills for things like: → Better User Interface → Better Typography → Better Colors → Better Layout → Better Accessibility ....& more Instead of relying on generic UI patterns, your AI agent can load the right design-engineering skill for the task at hand. If you're building modern frontends with AI coding agents, this is definitely worth bookmarking :) Source 🔗: github(DOT)com/jakubkrehel/skills Follow me for more amazing AI, Coding & Web Dev insights 💎

I wish I found this sooner. 🫡 This open-source collection of Agentic UI Skills teaches AI coding agents how to build interfaces that feel polished, not just functional. You'll find specialized skills for things like: → Better User Interface → Better Typography → Better Colors → Better Layout → Better Accessibility ....& more Instead of relying on generic UI patterns, your AI agent can load the right design-engineering skill for the task at hand. If you're building modern frontends with AI coding agents, this is definitely worth bookmarking :) Source 🔗: github(DOT)com/jakubkrehel/skills Follow me for more amazing AI, Coding & Web Dev insights 💎

Ram Maheshwari

14,955 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 1 Jahr

Announcing Chrome DevTools MCP! 🚀 Connect your AI coding agent to Chrome's powerful automation & debugging capabilities with ease. Key features: ✅ Reliable automation: It can programmatically handle clicks, form fills, dialogs, and page navigation with ease. ✅ Performance insights: Go beyond simple audits. Instruct your agent to record a performance trace and extract actionable insights to optimize your web apps. ✅ Advanced debugging: Empower your AI to analyze network requests, list console messages, take screenshots, and even evaluate scripts in the browser context. ✅ Browser emulation: Easily test different conditions by emulating CPU slowdowns, network throttling, or various screen sizes. Works well with modern web apps and believe this will unlock new workflows for automated testing, AI-driven debugging, and interactive web development. And there's much more to come!

Announcing Chrome DevTools MCP! 🚀 Connect your AI coding agent to Chrome's powerful automation & debugging capabilities with ease. Key features: ✅ Reliable automation: It can programmatically handle clicks, form fills, dialogs, and page navigation with ease. ✅ Performance insights: Go beyond simple audits. Instruct your agent to record a performance trace and extract actionable insights to optimize your web apps. ✅ Advanced debugging: Empower your AI to analyze network requests, list console messages, take screenshots, and even evaluate scripts in the browser context. ✅ Browser emulation: Easily test different conditions by emulating CPU slowdowns, network throttling, or various screen sizes. Works well with modern web apps and believe this will unlock new workflows for automated testing, AI-driven debugging, and interactive web development. And there's much more to come!

Addy Osmani

269,736 Aufrufe • vor 1 Jahr

🚀Introducing VisualWebBench: A Comprehensive Benchmark for Multimodal Web Page Understanding and Grounding. 🤔What's this all about? Why this benchmark? > Back in Nov 2023, when we released MMMU ( a comprehensive multimodal understanding benchmark, we received feedback that it included very few UI screenshots. Considering the growing importance of UI understanding, especially with the rise of powerful agents like Devin ( which is built on the strong vision capability of #GPT4, we recognized the need for a benchmark focused on UI screenshot understanding.📸👀 > Multimodal #LLMs have significantly boosted web agents' performance on benchmarks like Mind2Web and WebArena. For instance, the SeeAct agent ( showcases the power of integrating vision into web agents. However, these benchmarks primarily evaluate the end-to-end task execution ability of web agents rather than their understanding of web pages. 🌉 Bridging the Gap with VisualWebBench > To provide a comprehensive evaluation of multimodal LLMs' web page understanding capabilities, we introduce VisualWebBench. Our benchmark spans 139 websites 🌐 across 12 domains 🏷️ and 87 sub-domains 🔍, ensuring a diverse and representative dataset. It assesses MLLMs at three levels: website-level, element-level, and action-level 📊, and encompasses seven tasks designed to evaluate understanding, OCR, grounding, and reasoning abilities 🧠💡. 😮 Surprising Findings > 🎉 Open-source models are catching up: Even though closed-source MLLMs are still leading the leaderboard, we are happy to see open-source models like LLaVA 1.6 34B achieve comparable performance to Gemini Pro. > 🧠 Grounding ability, crucial for developing MLLM-based web applications, is a weakness for most MLLMs. > 🖼️ Importance of Image Resolution: The limited image resolution handling capabilities of most open-source MLLMs restrict their utility in web scenarios, where rich text and elements are prevalent. > 🧱 Relatively strong correlation with general understanding benchmarks like MMMU but weak correlation with web agent benchmarks like Mind2Web. Web agent benchmarks primarily evaluate the end-to-end task execution ability of web agents, which involves a series of actions to accomplish a goal. In contrast, VisualWebBench emphasizes evaluating the foundational skills of MLLMs such as understanding and grounding web page elements. 💡Fun Fact > Claude Sonnet is better than Opus on our benchmark :) 🎓 Conclusion > VisualWebBench serves as a valuable resource for the community, driving research and development in the field of multimodal web page understanding and grounding. As MLLMs continue to evolve and improve, we look forward to seeing new applications and breakthroughs. We believe that our benchmark will contribute to the development of more powerful MLLMs in the web domain, ultimately leading to a more intuitive and efficient user experience on the web. Kudos to the student leads Junpeng Liu Yifan Song and the team Bill Yuchen Lin, Wai Lam, Graham Neubig, Yuanzhi Li! 👏 Check out more details in the Junpeng's thread👇

🚀Introducing VisualWebBench: A Comprehensive Benchmark for Multimodal Web Page Understanding and Grounding. 🤔What's this all about? Why this benchmark? > Back in Nov 2023, when we released MMMU ( a comprehensive multimodal understanding benchmark, we received feedback that it included very few UI screenshots. Considering the growing importance of UI understanding, especially with the rise of powerful agents like Devin ( which is built on the strong vision capability of #GPT4, we recognized the need for a benchmark focused on UI screenshot understanding.📸👀 > Multimodal #LLMs have significantly boosted web agents' performance on benchmarks like Mind2Web and WebArena. For instance, the SeeAct agent ( showcases the power of integrating vision into web agents. However, these benchmarks primarily evaluate the end-to-end task execution ability of web agents rather than their understanding of web pages. 🌉 Bridging the Gap with VisualWebBench > To provide a comprehensive evaluation of multimodal LLMs' web page understanding capabilities, we introduce VisualWebBench. Our benchmark spans 139 websites 🌐 across 12 domains 🏷️ and 87 sub-domains 🔍, ensuring a diverse and representative dataset. It assesses MLLMs at three levels: website-level, element-level, and action-level 📊, and encompasses seven tasks designed to evaluate understanding, OCR, grounding, and reasoning abilities 🧠💡. 😮 Surprising Findings > 🎉 Open-source models are catching up: Even though closed-source MLLMs are still leading the leaderboard, we are happy to see open-source models like LLaVA 1.6 34B achieve comparable performance to Gemini Pro. > 🧠 Grounding ability, crucial for developing MLLM-based web applications, is a weakness for most MLLMs. > 🖼️ Importance of Image Resolution: The limited image resolution handling capabilities of most open-source MLLMs restrict their utility in web scenarios, where rich text and elements are prevalent. > 🧱 Relatively strong correlation with general understanding benchmarks like MMMU but weak correlation with web agent benchmarks like Mind2Web. Web agent benchmarks primarily evaluate the end-to-end task execution ability of web agents, which involves a series of actions to accomplish a goal. In contrast, VisualWebBench emphasizes evaluating the foundational skills of MLLMs such as understanding and grounding web page elements. 💡Fun Fact > Claude Sonnet is better than Opus on our benchmark :) 🎓 Conclusion > VisualWebBench serves as a valuable resource for the community, driving research and development in the field of multimodal web page understanding and grounding. As MLLMs continue to evolve and improve, we look forward to seeing new applications and breakthroughs. We believe that our benchmark will contribute to the development of more powerful MLLMs in the web domain, ultimately leading to a more intuitive and efficient user experience on the web. Kudos to the student leads Junpeng Liu Yifan Song and the team Bill Yuchen Lin, Wai Lam, Graham Neubig, Yuanzhi Li! 👏 Check out more details in the Junpeng's thread👇

Xiang Yue

56,697 Aufrufe • vor 2 Jahren

🚀 ZKX Wallet Web App is NOW LIVE! The ZKX Wallet | The Secure and Private Wallet 🗽 is no longer tied to specific operating systems or app stores. We’ve rebuilt the same codebase that powers our ZKX Extension into a fully functional web app. With this release, ZKX Wallet is now accessible on any device with a modern web browser: • iOS (Safari, Chrome, etc.) • macOS • Windows • Linux • Android • ChromeOS • …and basically anything else with a browser No native installation is required for core access. You can also install it as a Progressive Web App (PWA) for a more app-like experience on supported devices. Your keys remain fully in your control. Even your HOT wallet is protected with encrypted passkey-based security, with no backup dependency or identity linking. Railgun shielding works seamlessly, hardware wallet support (including QR-based signing) is included, and you get unified private and public balance management across EVM chains and PulseChain. We can now ship new features almost daily to improve the wallet experience for our users. Privacy. Security. Accessibility. Universal.

🚀 ZKX Wallet Web App is NOW LIVE! The ZKX Wallet | The Secure and Private Wallet 🗽 is no longer tied to specific operating systems or app stores. We’ve rebuilt the same codebase that powers our ZKX Extension into a fully functional web app. With this release, ZKX Wallet is now accessible on any device with a modern web browser: • iOS (Safari, Chrome, etc.) • macOS • Windows • Linux • Android • ChromeOS • …and basically anything else with a browser No native installation is required for core access. You can also install it as a Progressive Web App (PWA) for a more app-like experience on supported devices. Your keys remain fully in your control. Even your HOT wallet is protected with encrypted passkey-based security, with no backup dependency or identity linking. Railgun shielding works seamlessly, hardware wallet support (including QR-based signing) is included, and you get unified private and public balance management across EVM chains and PulseChain. We can now ship new features almost daily to improve the wallet experience for our users. Privacy. Security. Accessibility. Universal.

Liberty Swap | C.R.O.P.S. on PulseChain 🗽

34,990 Aufrufe • vor 3 Monaten

Introducing AI Assistance in Chrome DevTools! 🤯🚀 Ever wish you could have a coding buddy to help you with CSS and layout? Well, your wish is granted! The new AI Assistance in Chrome DevTools is here to make your life as a developer easier, powered by Gemini. The new panel aims to help you find and fix issues. Just ask it questions about your code, and it provides tailored explanations and even suggests solutions. This is our first exploration into agentic AI in DevTools and we're excited to hear what you think of it. Some key features: 1. Conversational prompts: Get tailored suggestions and code snippets. 2. Contextual understanding: AI understands the code you're working on. 3. Step-by-step guidance: Get help with everything from styling to debugging. Want to see it in action? Check out my screencast below to see how I used it to add a dark mode.

Introducing AI Assistance in Chrome DevTools! 🤯🚀 Ever wish you could have a coding buddy to help you with CSS and layout? Well, your wish is granted! The new AI Assistance in Chrome DevTools is here to make your life as a developer easier, powered by Gemini. The new panel aims to help you find and fix issues. Just ask it questions about your code, and it provides tailored explanations and even suggests solutions. This is our first exploration into agentic AI in DevTools and we're excited to hear what you think of it. Some key features: 1. Conversational prompts: Get tailored suggestions and code snippets. 2. Contextual understanding: AI understands the code you're working on. 3. Step-by-step guidance: Get help with everything from styling to debugging. Want to see it in action? Check out my screencast below to see how I used it to add a dark mode.

Addy Osmani

96,035 Aufrufe • vor 2 Jahren

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

46,142 Aufrufe • vor 1 Jahr

New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

Andrew Ng

140,307 Aufrufe • vor 1 Jahr

Introducing Sarvam Samvaad 🚀 Sarvam Samvaad, our Conversational AI Platform, is designed to help enterprises build, test, and launch AI agents fluent in 11 Indian languages. ▪️Power interactions across telephone, WhatsApp, web, and apps ▪️Handle complex phrases, alphanumerics, and proper nouns with precision ▪️Listen in on every conversation and discover deep insight ▪️Scale your support with pricing built for India Trusted by leading brands across the country, the platform delivers production-ready agents so enterprises can move from pilot to full-scale deployment in just days. It’s time to level up customer experience with Sarvam Samvaad. Watch it in action ↓

Introducing Sarvam Samvaad 🚀 Sarvam Samvaad, our Conversational AI Platform, is designed to help enterprises build, test, and launch AI agents fluent in 11 Indian languages. ▪️Power interactions across telephone, WhatsApp, web, and apps ▪️Handle complex phrases, alphanumerics, and proper nouns with precision ▪️Listen in on every conversation and discover deep insight ▪️Scale your support with pricing built for India Trusted by leading brands across the country, the platform delivers production-ready agents so enterprises can move from pilot to full-scale deployment in just days. It’s time to level up customer experience with Sarvam Samvaad. Watch it in action ↓

Sarvam

222,970 Aufrufe • vor 1 Jahr

The #1 use case of web browsing agents isn't ordering food/booking flights it's using other agentic platforms. instead of human in the loop you have *another* agent in the loop. this creates a semi autonomous agent. this is probably how we get to agi: - vertically agents (doing one thing really well) - glued together by everything api (web browser) most llm providers will probably vertically integrate. i.e good chance we'll see "operating agents" running thousands of chatgpt/claudes instances on your behalf. ex: just watched operator using an ai spreadsheet (Otto ) to research, store and then use the data for creating a powerpoint. the only thing it needs to worry about is the high level goal and moving things around.

The #1 use case of web browsing agents isn't ordering food/booking flights it's using other agentic platforms. instead of human in the loop you have another agent in the loop. this creates a semi autonomous agent. this is probably how we get to agi: - vertically agents (doing one thing really well) - glued together by everything api (web browser) most llm providers will probably vertically integrate. i.e good chance we'll see "operating agents" running thousands of chatgpt/claudes instances on your behalf. ex: just watched operator using an ai spreadsheet (Otto ) to research, store and then use the data for creating a powerpoint. the only thing it needs to worry about is the high level goal and moving things around.

Sully

77,898 Aufrufe • vor 1 Jahr

What happens when there are more agents than eyeballs browsing the internet? How will the economics of the internet be reinvented? Parag Agrawal thinks Shapley values may hold the answer. Parag built Twitter over a decade, eventually becoming CEO and selling the company to Elon. He's spent the last three years building Parallel Web Systems, a search engine built for agents instead of humans. His core argument: 1) human click data is a bug. Agents aren't just a new technology, they're a distinct customer – and the feedback loop that made Google great is the wrong signal for the thing actually doing the work; 2) the ad-funded web assumed scarce human attention. If agents show up instead of eyeballs, the business model underneath the internet has to be rebuilt, or good content stops being published. The conversation covers: — why he shipped a search agent before a search engine, and how that let him grow the index incrementally instead of buying a full web crawl up front — the billion-to-billion matching problem: pull the right 1,000 tokens out of a trillion web pages, and your agent uses under half the tokens — going from a 3-second compute budget to 200 milliseconds — why he doesn't think Parallel is a neo-lab: "our output is a complement to a model" — the Google Cloud deal — on GCP, your grounding options are now Google Search or Parallel Search — reinventing the economics of the internet, Shapley values as a payment rail for publishers, and why the company was originally incorporated as Shapley Inc — why routing 2-10% of inference spend to web data would dwarf every content business outside the walled gardens — the web going from pull to push: "call me if this happens" 00:00 Introduction 03:25 What Is Web Search 05:17 Why Start a New Index 07:52 Search Agents First 10:17 Not a Neolab 13:14 Agents vs Google Search 19:38 Inside the Search Stack 28:59 Search Multipliers With Agents 30:21 Meeting Prep Agent Workflows 31:46 Quality Cost Latency And Turbo 32:42 Are Agents Overtaking Humans 34:28 Ads Model Meets Agent Web 37:20 New Incentives For Content 40:48 Shapley Values Attribution 47:46 Parallel Web And Future Vision Hosted with my very unwilling co-host Andrew Reed and Sequoia Capital

What happens when there are more agents than eyeballs browsing the internet? How will the economics of the internet be reinvented? Parag Agrawal thinks Shapley values may hold the answer. Parag built Twitter over a decade, eventually becoming CEO and selling the company to Elon. He's spent the last three years building Parallel Web Systems, a search engine built for agents instead of humans. His core argument: 1) human click data is a bug. Agents aren't just a new technology, they're a distinct customer – and the feedback loop that made Google great is the wrong signal for the thing actually doing the work; 2) the ad-funded web assumed scarce human attention. If agents show up instead of eyeballs, the business model underneath the internet has to be rebuilt, or good content stops being published. The conversation covers: — why he shipped a search agent before a search engine, and how that let him grow the index incrementally instead of buying a full web crawl up front — the billion-to-billion matching problem: pull the right 1,000 tokens out of a trillion web pages, and your agent uses under half the tokens — going from a 3-second compute budget to 200 milliseconds — why he doesn't think Parallel is a neo-lab: "our output is a complement to a model" — the Google Cloud deal — on GCP, your grounding options are now Google Search or Parallel Search — reinventing the economics of the internet, Shapley values as a payment rail for publishers, and why the company was originally incorporated as Shapley Inc — why routing 2-10% of inference spend to web data would dwarf every content business outside the walled gardens — the web going from pull to push: "call me if this happens" 00:00 Introduction 03:25 What Is Web Search 05:17 Why Start a New Index 07:52 Search Agents First 10:17 Not a Neolab 13:14 Agents vs Google Search 19:38 Inside the Search Stack 28:59 Search Multipliers With Agents 30:21 Meeting Prep Agent Workflows 31:46 Quality Cost Latency And Turbo 32:42 Are Agents Overtaking Humans 34:28 Ads Model Meets Agent Web 37:20 New Incentives For Content 40:48 Shapley Values Attribution 47:46 Parallel Web And Future Vision Hosted with my very unwilling co-host Andrew Reed and Sequoia Capital

Sonya Huang 🐥

129,143 Aufrufe • vor 1 Monat

Today we're thrilled to introduce Jace, your AI employee. Jace goes beyond AI chatbots by being able to handle longer-running tasks and taking actions in the digital world. By using our new AWA-1 (Autonomous Web Agent) model, Jace can use a browser to interact with websites just like any human would. This allows it to handle real-world tasks like researching and booking flights, handling a hiring process, or even setting up a company. We can't wait to see how Jace will help you! Join us at See some examples of what Jace can do in this thread:

Today we're thrilled to introduce Jace, your AI employee. Jace goes beyond AI chatbots by being able to handle longer-running tasks and taking actions in the digital world. By using our new AWA-1 (Autonomous Web Agent) model, Jace can use a browser to interact with websites just like any human would. This allows it to handle real-world tasks like researching and booking flights, handling a hiring process, or even setting up a company. We can't wait to see how Jace will help you! Join us at See some examples of what Jace can do in this thread:

Viktor

199,233 Aufrufe • vor 2 Jahren

How well do today’s frontier models handle long-horizon, multi-step web agent tasks, such as identifying the top 25 U.S. CS PhD programs with ML/AI faculty likely accepting students and compiling the results into a structured sheet? Check out our new work on Odysseys: Benchmarking Web Agents on Realistic Long Horizon Tasks Paper: Leaderboard: We introduce Odysseys, a benchmark of 200 long-horizon tasks derived from real browsing sessions and evaluated on the live Internet. We show that binary pass/fail is inadequate in this setting and propose rubric-based evaluation, which better aligns with human judgment and provides more informative signals. Across leading models, the best achieves only 44.5% success, leaving substantial headroom. We further introduce a Trajectory Efficiency metric (rubric score per step) and find efficiency remains extremely low (1.15%), highlighting a key bottleneck. Odysseys provides a realistic benchmark for measuring progress toward web agents capable of sustained, efficient, real-world operation. See a more detailed thread by Jing Yu Koh.

How well do today’s frontier models handle long-horizon, multi-step web agent tasks, such as identifying the top 25 U.S. CS PhD programs with ML/AI faculty likely accepting students and compiling the results into a structured sheet? Check out our new work on Odysseys: Benchmarking Web Agents on Realistic Long Horizon Tasks Paper: Leaderboard: We introduce Odysseys, a benchmark of 200 long-horizon tasks derived from real browsing sessions and evaluated on the live Internet. We show that binary pass/fail is inadequate in this setting and propose rubric-based evaluation, which better aligns with human judgment and provides more informative signals. Across leading models, the best achieves only 44.5% success, leaving substantial headroom. We further introduce a Trajectory Efficiency metric (rubric score per step) and find efficiency remains extremely low (1.15%), highlighting a key bottleneck. Odysseys provides a realistic benchmark for measuring progress toward web agents capable of sustained, efficient, real-world operation. See a more detailed thread by Jing Yu Koh.

Russ Salakhutdinov

22,518 Aufrufe • vor 5 Monaten

.Postman is now natively available in your favorite coding agent! The new Postman CLI is designed to bring API engineering for agents: 1️⃣ Works in Git and your code repo - Just drop Postman where your agent lives and works. 2️⃣ No GUI - Work in your coding agent. Bring up the Postman UI for review and approval. 3️⃣ No sign-up - Get started right away. Sign-up when you have to save your work and want more. Install once, work in your agent. Run npm install -g postman-cli, then postman init. This loads Postman's skills into your coding agent so it can create collections, write tests, and much more. Available as a plugin for all coding agents! The skills cover the API lifecycle: find APIs your org already has, generate tests that catch regressions, mock an API before it exists, watch live endpoints for downtime and drift, run checks in CI on every commit, and run performance tests. You can also extend the abilities of your agent with the Context Graph. An agent often sees only the code that is accessible to them. The Context Graph API gives it a map of your team's infrastructure, APIs, and services, and how they depend on each other. As your agent works, you stay the reviewer. Your agent does the work from the command line. Open Postman when you want to review or approve it. Check out more at

.Postman is now natively available in your favorite coding agent! The new Postman CLI is designed to bring API engineering for agents: 1️⃣ Works in Git and your code repo - Just drop Postman where your agent lives and works. 2️⃣ No GUI - Work in your coding agent. Bring up the Postman UI for review and approval. 3️⃣ No sign-up - Get started right away. Sign-up when you have to save your work and want more. Install once, work in your agent. Run npm install -g postman-cli, then postman init. This loads Postman's skills into your coding agent so it can create collections, write tests, and much more. Available as a plugin for all coding agents! The skills cover the API lifecycle: find APIs your org already has, generate tests that catch regressions, mock an API before it exists, watch live endpoints for downtime and drift, run checks in CI on every commit, and run performance tests. You can also extend the abilities of your agent with the Context Graph. An agent often sees only the code that is accessible to them. The Context Graph API gives it a map of your team's infrastructure, APIs, and services, and how they depend on each other. As your agent works, you stay the reviewer. Your agent does the work from the command line. Open Postman when you want to review or approve it. Check out more at

Abhinav Asthana

493,402 Aufrufe • vor 17 Tagen

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.

Santiago

21,079 Aufrufe • vor 2 Monaten

The next big thing is Asynchronous Coding Agents. Such Agents change our role as engineers from Conductor (directing one agent at a time) to Orchestrator (defining tasks for a "fleet" of agents working in parallel), for very well-defined tasks with human review. GitHub's Copilot Agent, as covered at #githubuniverse, is one such example I've been trying and liking. There are a number of options also worth considering, including Jules (also great!), Codex web by OpenAI Developers, and Anthropic's Claude Code for web. Editors like Cursor are also exploring this with some very interesting UX ideas. For the more advanced, orchestrators like Claude Squad, Magnet and Conductor by (nice balance on the engineering/product spectrum) are certainly worth considering. Some caveats: Orchestration has been working well for me in small-medium sized projects: for fixing UI bugs, adding small features etc. This can be good for tackling migrations, upgrading dependencies etc. Enterprise teams are very much still figuring out their "Conductor" patterns but eventually, I do see more orchestration becoming a standard part of the engineering workflow.

The next big thing is Asynchronous Coding Agents. Such Agents change our role as engineers from Conductor (directing one agent at a time) to Orchestrator (defining tasks for a "fleet" of agents working in parallel), for very well-defined tasks with human review. GitHub's Copilot Agent, as covered at #githubuniverse, is one such example I've been trying and liking. There are a number of options also worth considering, including Jules (also great!), Codex web by OpenAI Developers, and Anthropic's Claude Code for web. Editors like Cursor are also exploring this with some very interesting UX ideas. For the more advanced, orchestrators like Claude Squad, Magnet and Conductor by (nice balance on the engineering/product spectrum) are certainly worth considering. Some caveats: Orchestration has been working well for me in small-medium sized projects: for fixing UI bugs, adding small features etc. This can be good for tackling migrations, upgrading dependencies etc. Enterprise teams are very much still figuring out their "Conductor" patterns but eventually, I do see more orchestration becoming a standard part of the engineering workflow.

Addy Osmani

17,510 Aufrufe • vor 11 Monaten

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

GREG ISENBERG

135,254 Aufrufe • vor 6 Monaten

Lauren Tan is shipping 1000 of PRs a month with AI agents Matt Pocock just sat down with her to unpack how the system actually works: · 04:35 - how Lauren got to thousands of PRs without drowning in AI slop · 13:20 - why pstack starts with skills, not bigger prompts · 24:10 - verification: make the agent prove its work before you trust it · 36:45 - redesign the codebase so agents naturally take the right path · 49:30 - how to turn one coding agent into a high-velocity software factory the biggest lesson isn’t “run more agents.” it’s making one agent trustworthy enough that you can safely run 10, 20, then hundreds of tasks in parallel. skills → constraints → verification → cloud agents → parallel execution Lauren basically treats the codebase as infrastructure for the agents themselves. Humans decide what matters. Agents do the work, prove it works, and keep the loop moving. Probably one of the best conversations on agentic engineering right now. watch it before you add another 20-page prompt to your coding agent.

Lauren Tan is shipping 1000 of PRs a month with AI agents Matt Pocock just sat down with her to unpack how the system actually works: · 04:35 - how Lauren got to thousands of PRs without drowning in AI slop · 13:20 - why pstack starts with skills, not bigger prompts · 24:10 - verification: make the agent prove its work before you trust it · 36:45 - redesign the codebase so agents naturally take the right path · 49:30 - how to turn one coding agent into a high-velocity software factory the biggest lesson isn’t “run more agents.” it’s making one agent trustworthy enough that you can safely run 10, 20, then hundreds of tasks in parallel. skills → constraints → verification → cloud agents → parallel execution Lauren basically treats the codebase as infrastructure for the agents themselves. Humans decide what matters. Agents do the work, prove it works, and keep the loop moving. Probably one of the best conversations on agentic engineering right now. watch it before you add another 20-page prompt to your coding agent.

Morlex

22,614 Aufrufe • vor 14 Tagen

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:

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

118,297 Aufrufe • vor 1 Jahr