Apify’s Post

Introducing our public preview of Chrome DevTools Model Context Protocol (MCP), a new way to bring the power of Chrome DevTools directly to your AI agent → https://goo.gle/4pDE6Tk With it, you can: 📈 Run performance traces and automate performance audits ✅ Verify real-time code changes & fixes ⚙️ Diagnose network and console errors, and understand why a feature isn't working as expected ... and so much more! Join our public preview and share your feedback on which capability we should add next.

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.

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

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?

��� Automating Smarter with n8n: The Power of the Text Checker Node One of the most underrated nodes in n8n is the 'Text Checker'. It gives your workflows the ability to analyze text and branch logic based on conditions like keywords, regex patterns, or string matches essentially turning raw text into actionable decisions. In my recent experiment, I combined Gmail with a Text Classifier: incoming emails get checked, and if flagged as spam, the workflow automatically adds the “SPAM” label in Gmail, safe emails continue through an AI agent for further processing. A simple yet powerful way to keep your inbox clean. ✨ Small automation, big impact. What’s one workflow where you’ve used text analysis to save time? #n8n #iPaas #textchecker #aiAutomation

🌟 Sunday Spotlight 🌟 This week’s spotlight is on our official n8n integration. With it, developers can: ✅ Query SurrealDB directly inside n8n workflows ✅ Orchestrate AI pipelines across 350+ services ✅ Trigger automations from live SurrealDB events. Docs 👉 https://sdb.li/3I9iflD Repo 👉 https://sdb.li/3UZxtwB Blog 👉 https://sdb.li/4p6cRAu

Vector databases are only as good as the context you feed them. But keeping attributes fresh—like user permissions, priority scores, or SLA breaches—has always been a choice between stale data or expensive compute. That tradeoff ends with Materialize. By incrementally maintaining attributes as source data changes, Materialize keeps your vector database pipelines fresh and efficient. The result: • Better recall through accurate filtering and reranking • Lower costs by avoiding wasteful re-computation • Simpler pipelines without the fragile web of replicas, caches, and queues Whether you’re powering semantic search or complex agent workflows, Materialize makes sure your AI initiatives act confidently on live context. 👉 Read the full post to see how it works with Turbopuffer and customer support use cases: https://bit.ly/3IyGY2Z