Agentic AI is beginning to reshape malware detection and broader security operations. These systems are being used not to replace humans, but to take on the lower value jobs that have historically tied up analysts — from triaging alerts to reverse-engineering suspicious files.
Google has introduced LangExtract, an open-source Python library designed to help developers extract structured information from unstructured text using large language models such as the Gemini models. The library simplifies the process of converting free-form text into structured data, offering features like controlled generation, text chunking, parallel processing, and integration with various LLMs.
Opal is a new experimental tool from Google Labs that lets you build and share powerful AI mini apps that chain together prompts, models, and tools — all using simple natural language and visual editing. It's currently in public beta in the US.
Google is integrating Gemini Gems into Workspace apps like Docs, Sheets, and Gmail, allowing users to access customizable AI chatbots directly within these applications.
Google is replacing direct website links with its own 'search.app' or 'share.google' URLs when articles are shared directly from the Discover feed. To clarify the destination, shared links now include a message with the article's title, source, and a "Shared via Google" note.
This post explores how developers can leverage Gemini 2.5 to build sophisticated robotics applications, focusing on semantic scene understanding, spatial reasoning with code generation, and interactive robotics applications using the Live API. It also highlights safety measures and current applications by trusted testers.
A review of a Google paper outlining their framework for secure AI agents, focusing on risks like rogue actions and sensitive data disclosure, and their three core principles: well-defined human controllers, limited agent powers, and observable actions/planning.
Google today announced that the SDK for its Gemini models will natively support the Model Context Protocol from Anthropic. This move aims to simplify the connection between AI agents and data sources, aligning with the growing popularity of MCP and complementing Google's own Agent2Agent protocol. The company also plans to ease deployment of MCP servers and hosted tools for AI agents.
A Google engineer's testimony shows how page quality is scored and confirms the existence of a popularity signal that uses Chrome data.
