Announcing Chrome DevTools MCP! 🚀 Connect your AI coding agent to...

Uploaded: 2025-09-23T14:59:26.000Z
Duration: PT214.833S
Channel: Addy Osmani

7:28

This MCP tool for Cursor will literally save you HOURS of debugging 🔥🦾 With BrowserTools v1.2 you can… ✅ Analyze all browser logs w/ deep reasoning ✅ Capture + auto-paste screenshots into Cursor ✅ Run accessibility, SEO + web app audits ✅ Fully optimize your NextJS app for SEO ✅ Make changes to a selected element Watch the full video for installation steps + check my bio to access our docs 👇 BIG thanks to Emil for implementing the auditing capabilities!! If you have any ideas or feature requests, please reply down below and we’ll add them to the backlog 🙌

Ted Werbel

78,784 views • 1 year ago

0:28

WTF Brooooooooooo... why didn't I find this earlier? 🤯 This browser extension lets you add multiple devices with different screen sizes directly to a webpage to test responsive designs. It comes with tons of customization options, and you can even sync scrolling across all your device previews simultaneously. Perfect for UI developers, designers, vibe coders, or anyone using AI to ship web products :) Follow me for more amazing AI, Coding & Web Dev insights 💎

Ram Maheshwari

60,295 views • 17 days ago

0:40

wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.

Molly O’Shea

20,408 views • 1 year ago

0:53

Today we are launching something BIG! The most accurate and fastest web search APIs for AI agents Something which took us months to build Something so powerful that it lets your AI agent to go from "guessing" -> "doing" Combined with our powerful people and company APIs, your AI agent can now find actionable answer to any question and finally act with confidence Whether you are building an AI agent in sales, recruitment, investment, insurance or just adding small automation which needs access to web data in structured manner, this is for you. Live on Producthunt now! Do upvote and support (link in comments)

Abhilash Chowdhary

32,914 views • 7 months ago

1:00

Type AI Web is HERE! 🌟 After rigorous testing in beta, Type AI Web is officially launching to the public on January 13, 2025! 🗓️ All features over telegram bot, now on a sleek web interface: ⚡️ Analyze your portfolio 📈 View real-time charts 🔄 Swap tokens with ease 🌉 Bridge assets across chains 💸 Invest in trending projects 🔗 Transfer assets securely 🔍 Fetch detailed token insights ✅ Audit your projects 💵 On/Off Ramp with ease and a lot more... 🌐 Expanding Access: Type AI Web opens the doors to a broader audience, empowering everyone to interact seamlessly with crypto ecosystem. 💰 More Revenue Streams: With new advertising spaces, Type AI continues to create value for $TYPE holders while diversifying revenue streams. 🔒 Connect your existing wallet securely on Type AI Web without even sharing your private keys (advantage over telegram bot) #TypeAI #ETH

Type AI

34,551 views • 1 year ago

3:52

Build your own Gen AI Agents with Salesforce’s new Agentforce platform—anyone can create, test, and scale custom Gen AI Agents using our unique Einstein1 low-code platform—deeply integrated with our Einstein1 platform. The future of AI Agent and App Dev is in your hands! With Agentforce, your custom AI agents: - Achieve greater accuracy by securely grounding in all your enterprise data with Data Cloud - Take action seamlessly by leveraging Flow automations - Connect effortlessly to your enterprise APIs through MuleSoft - Test with confidence in Sandboxes before deployment - Use in your apps, web, social, and Slack. Humans with AI drive customer success, together. Now everyone is an Einstein. More to come at Dreamforce 2024! 💙

Marc Benioff

70,985 views • 2 years ago

7:00

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

96,000 views • 1 year ago

4:01

AI coding agents aren't just about autocorrect, but how do you get the best coding experience with an AI-powered coding agent? In our new short course, Build Apps with Windsurf’s AI Coding Agents, you'll learn how to build, debug, and deploy applications with agentic AI-powered integrated development environment (IDE). AI coding agents, like Codeium's @windsurf, don’t just suggest code, they analyze your codebase, track changes, retrieve relevant information, and apply updates across multiple files. They can help debug, refactor, and even modernize legacy frameworks. But to use them effectively, you need the right approach. This new course shows you how to: 🛠️ Use AI agents to build and refine applications, like a Wikipedia analysis app. 🐞 Debug and refactor JavaScript with AI-assisted automation. 🔍 Understand how search and retrieval power AI coding agents. 🤖 Guide an AI agent effectively—prompting, iterating, and correcting when needed. Taught by Anshul Ramachandran (Anshul Ramachandran), this course gives you hands-on coding experience, insights into how these AI systems work under the hood, and best practices to improve your development workflow. 🔗 Enroll for free:

DeepLearning.AI

23,255 views • 1 year ago

7:10

Today, we’re shipping new ways to observe, analyze, and debug agents with LangSmith: • Polly: an AI assistant for AI engineering that helps you understand traces, threads, and improve prompts • LangSmith Fetch: a CLI for pulling trace & thread data straight into your terminal or coding agent Agents are running longer and getting more complex, which demands new debugging workflows beyond simple LLM apps. We wrote a blog on the trends behind this shift— and why tools like Polly and LangSmith Fetch are needed. Shipping reliable agents requires full visibility into agent behavior, with tooling that helps you reason over that data in the UI, the terminal, or alongside coding agents. 📔Learn more about Polly: 📔Learn more about LangSmith Fetch: 📔How to observe deep agents: 📽️ Polly video tutorial: 📽️ LangSmith Fetch video tutorial:

LangChain

31,035 views • 8 months ago

0:58

Imagine if your browser had a brain. And it could click, read, and act like you. That’s exactly what flowithOS by does. It’s not an AI tool. ↳ It’s an Agentic Operating System right inside your browser. You browse as usual. The agent does everything else. → Reads pages and pulls insights → Writes summaries or reports → Executes tasks across tabs and apps → Even manages your workflow while you focus on decisions I used it to automate my newsletter content creation. 1. I wrote one prompt 2. It searched the web for top AI news 3. It created a content doc, and organized everything for me. Think of it like fully automated self-driving, but for the internet. Instead of adding AI features to a browser, flowithOS rebuilt the entire experience. It combines vision, reasoning, and execution in one loop. Your intent becomes action in real time. Use it to research, create, manage content, shop, study, or build projects. One space. One agent. Infinite actions. 📌 Try flowithOS today → ---- #flowithOS #AI #Agent #FutureOfWork #Productivity #flowithOSPartnership #SponsoredByflowithOS

Andrew Bolis

36,689 views • 10 months ago

3:02

Codex 0.93 is here, and with it, shiny new features! App Connectors have arrived. Connect to an array of cloud apps directly from your terminal. No config files. No setting up MCP servers, or hunting down docs. Just two clicks, and you're off! Github, Notion, Google Apps, Microsoft Apps, Vercel, Adobe, Canva, Dropbox, Expedia, Figma, Coursera, Hubspot, Linear, Monday, Instacart, SendGrid, Resent, Stripe, Target, and Peleton? Plus more. OpenAI is going for a unified experience from cloud to terminal, and they unlock a bunch of capabilities for your terminal agent. I believe with this direction they're going, they are truly leaning into a complete AI assistant, not just for coding. While these capabilities are not exactly new, they are much easier to install and use, greatly improving the UX for everyone. I for one, appreciate it. To get started, update Codex, and type /experimental. Turn on "Apps" and restart your session. If you want more videos like this, please drop a comment!

am.will

34,023 views • 7 months ago

10:59

OpenApp Staking & App Listing Pi Network OpenApp Nicolas Kokkalis Chengdiao Fan Pi Network is home to millions of engaged Pioneers actively exploring apps, services, and digital opportunities within the ecosystem. For developers, creators, and businesses, one of the biggest challenges is visibility—how can great apps reach the right users? OpenApp Staking & App Listing was created to solve that challenge. OpenApp provides a decentralized discovery platform where developers can list their Pi-powered applications and use staking to increase their visibility within the OpenApp directory. By staking, apps gain greater exposure to the community, helping attract more users, engagement, and growth opportunities. This creates a self-service ecosystem where builders can leverage Pi to promote their projects while allowing Pioneers to discover innovative applications more easily. As technology and AI continue to make app development faster and more accessible, the real challenge becomes user acquisition and discovery. OpenApp bridges that gap by providing: ✅ App Listing & Discovery ✅ Directory Staking for Increased Visibility ✅ Featured App Promotions ✅ Category-Based Rankings ✅ Pioneer-Friendly App Exploration ✅ Support for Mainnet, Testnet, and Beta Apps Whether you're launching a new project or growing an existing one, OpenApp helps connect your application with the Pi community and gives users a simple way to discover the best apps in the ecosystem. 🚀 List Your App: 📚 Learn More: OpenApp — Discover. Publish. Grow.

Mrwain Organization

13,868 views • 2 months ago

5:38

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Santiago

101,473 views • 1 year ago

1:03

If you’ve never written code before, this is for you. I’ve just launched a course that shows you, in less than 30 minutes, how to describe an idea for an app and build it with AI. In this course, you'll build a working web application - a funny interactive birthday message generator that runs in your browser and can be shared with friends. You'll customize it by telling AI how you want it changed, and tweak it until it works the way you want. By the end, you'll have a repeatable process you can apply to build a wide variety of applications. If you want to try vibe coding, this will be the best place to start! Further, you'll be able to use these techniques with whatever tool you're most comfortable with (like ChatGPT, Gemini, Claude, or others) -- we're vendor neutral. Skills you'll gain: - How to build web apps with AI - zero coding skills needed - How to fix and improve your creations by chatting with AI - A simple process you can use to build other things you can dream up Building with AI is one of the most fun things in the world. Please join me and take your first step! I think you will be surprised at what you can build. And if you're an experienced engineer, please share this with someone in your life who's been curious about building with AI. Come build with me!

Andrew Ng

637,189 views • 8 months ago

2:43

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,227 views • 1 year ago

0:38

🚂 Introducing Agent Prompt Train 🚂 An open-sourced high-performance proxy that works with Claude Code to help turn it from a single-developer tool into a collaborative development platform. Claude Code changed the game. Now, Agent Prompt Train is here to help teams: ✅ Log every conversation for review ✅ Analyze & optimize prompts with AI ✅ Turn best practices into reusable team commands ✨ The payoff: Smarter prompts, faster learning, and shared knowledge across your whole team. 📖 Read more on the blog & test it out 👇

Moonsong Labs

21,500 views • 1 year ago

2:06

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

3:25

🚨 BREAKING: The first-ever agentic browser is here — and it's shockingly good. Just tried Fellou, an AI browser that doesn’t assist you with browsing, it does the browsing for me. It's like Chrome but with a brain—AI agents handle deep research and workflows solo. Handles several projects in parallel. A top-tier AI intern — takes care of all the dirty and tedious work, so you don’t have to and its 100% Free 1️⃣Fellou’s not just another browser—it's an Agentic assistant that acts for you. 2️⃣It handles real tasks autonomously: research, cross-platform flows, and full automation. 3️⃣ Past browsing. Into real action. Fellou can automatically plan tasks, invoke tools, and execute actions to coordinate operations across multiple web interfaces, enabling various in-browser tasks. These include shopping, scheduling meetings, sending emails, and posting tweets based on webpage content. It’s the first Agentic Browser — with deep research, tab-level collaboration, and seamless automation. Deep Search acts like a smart intern: spins up five shadow browsers, digs across web and private platforms, and compiles richer insights fast. Highlights gaps and surfaces info you missed. Runs in parallel, won’t slow anything down. Automated workflows: Replaces manual clicking with invisible ops across pages. Reduces drag, frees up hours. Automation-aware browsing: Ask the page questions, reuse content in your drafts. 🧵 1/n

Rohan Paul

55,351 views • 1 year ago

2:07

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

126,587 views • 1 year ago

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