Chrome just announced a new tool to help coding agents access the full power of chrome DevTools. Your agent can now verify your code changes in real-time, diagnose errors and perform performance audits!

I've been testing since earlier versions and this is a great tool to add to your coding agents. Giving your coding agent what is effectivelly expert level knowledge and access to your browser has never been easier, and allows for some pretty interesting workflows. The first thing you will want to do is get familiar with context engineering techniques to manage context on the main agent since insights and telemetry data can get pretty extensive as result of the tools used. Learn how to use sub-agents to do the debugging and telemetry and insight gathering, generating report files with the information needed for fixes (not only insights and metrics but affected files and code snippets) for powerful 'self-correcting' workflows. You can comprise most of this under specialized prompts (/ commands on Cursor for instance). This is a great initiative by the Chrome for Developers team and brings expert insights to developers of all levels!

The Chrome DevTools MCP allows you to generate a fix with Gemini CLI and then automatically verify that the solution works as intended. Full set of tools - https://lnkd.in/gYiwstQF You can now - - Empower your AI agent to analyze network requests to uncover CORS issues or inspect console logs to understand why a feature isn't working as expected. - Navigate, fill out forms, and click buttons to reproduce bugs and test complex user flows—all while inspecting the runtime environment. - Ask your AI agent to connect to a live page, inspect the DOM and CSS, and get concrete suggestions to fix complex layout problems like overflowing elements based on live data from the browser. - Instruct your AI agent to run a performance trace, analyze the results, and investigate specific performance issues like high LCP numbers. Rajat Arun Mohan Richard Alok Richard Wei Yih Yap Salomone Derrick Andrew Marcel Rajan Manikantan #ChromeDev #ChromeDevToolsMCP #GeminiCLI #Google #BrowserUse #PublicPreview

The new Chrome DevTools MCP preview is a game-changer for anyone using AI to build for the web. Say goodbye to the tedious copy-paste of screenshots and HTML. Your AI coding assistant can now directly interact with your web page, analyze network requests, inspect the DOM, and even simulate user behavior. This is a huge step forward in AI-powered development. It resonates with some of the work my teams are doing to simplify complex APIs use with LLMs. Check out the announcement and get ready for a smarter, faster debugging workflow.

New Demo 🔥 FedAI Savant demo lets users explore Federal Reserve policy through an interactive chat interface. Instead of reading long FOMC reports, users can simply ask: → “What was the Fed’s last rate decision?” → “Compare June vs July 2024 meetings” → “What economic factors drove the decision?” Powered by: ⚡ Gradio + gr.ChatInterface for the frontend ⚡ Structured dataset updates (latest: Aug 2025) ⚡ Fireworks AI inference for fast, reliable answers The demo shows how domain-specific data can be turned into an AI assistant without heavy infrastructure. 👉 Try it here: https://lnkd.in/gWTUJSyv What dataset would you adapt this approach to in your own vertical?

As scary as it sounds, manual intervention are relevant in production releases and deployment. Models are gaining more agency with access to api tools, IDEs, terminals etc. Small resistance probability may compound in multi step deployment, enabling cascading failures like unauthorized persistence or resource hoarding. AI agents that won't turn off -thats terrifying. Arising in a situation where AI coherence trumps human intent. Learn more: https://lnkd.in/dQ7jEWVQ

If you’re wondering what I’ve been building lately for Razroo | Synthetic Data & AI Simulations : This past week I’ve been integrating tools in ways I’ve never done before for makebind .com. It’s been an incredibly arduous process but also seamless because it has to be done in a way the user never leaves the chat. Things like web sockets, jwt tokens, mini frontends, dedicated vms, dedicated AI agents, specialized webhooks for 3rd party integrations, and AI assistant switch case statements in frontend to trigger events if need be. db tables to integrate them all with each other in a secure, scalable and safe fashion(which architected db table its own thing). However, once complete, which it seems like will, it opens up the door for a lot more people to join the process. Combine that with data, well you can do some pretty incredible things in the enterprise space. Keep an eye out

Use 6,000+ Apify Actors in your n8n workflows ⚡️ Your agents are only as smart as the data they get. Feed them real-time web data with zero scraping overhead - straight from Apify into n8n. We’ve upgraded our integration so that you can now run any Apify Actor via tool call in the AI agent node. One practical example: create an agent to monitor brand mentions, analyze their sentiment, and decide whether to amplify praise or alert support to handle complaints. Plus, from now on you can run Actors synchronously and get the output in the same step. 🩷 Find us in n8n, by typing “Apify” in the node selector menu. Plus link in the comments.

Time for the next version of Aici VS Code Extension - now it supports: /update — AI automated file updates /commit — AI-generated git commit messages /plan — AI file change plans /build — AI-assisted build commands with error fixes https://lnkd.in/gSJt2Fuv

MotiaDev/motia: Modern Backend Framework that unifies APIs, background jobs, workflows, and AI Agents into a single core primitive with built-in observability and state management. https://lnkd.in/d4CEEMWH