World Wire

Google DeepMind research team introduced Gemini Robotics-ER 1.6, a significant upgrade to its embodied reasoning model designed to serve as the ‘cognitive brain’ of robots operating in real-world environments. The model specializes in reasoning capabilities critical for robotics, including visual and spatial understanding, task planning, and success detection — acting as the high-level reasoning model for a robot, capable of executing tasks by natively calling tools like Google Search, vision-language-action models (VLAs), or any other third-party user-defined functions. Here is the key architectural idea to understand: Google DeepMind takes a dual-model approach to robotics AI. Gemini Robotics 1.5 is the vision-language-action (VLA) model — it processes visual inputs and user prompts and directly translates them into physical motor commands. Gemini Robotics-ER, on the other hand, is the embodied reasoning model: it specializes in understanding physical spaces, planning, and making lo...

Google just announced the release of Skills in Chrome , a new feature built into Gemini in Chrome that lets users save frequently used AI prompts as reusable, one-click workflows called Skills. The rollout begins April 14, 2026, targeting Mac, Windows, and ChromeOS users who have their Chrome language set to English-US. If you’ve been paying attention to how AI is being woven into operating systems and browsers over the past year, Skills in Chrome represents something more interesting than just a productivity shortcut — it’s an early glimpse at how prompt management and browser-level AI agents could converge. The Problem It Solves Anyone who has used Gemini in Chrome for routine tasks knows the friction: every time you navigate to a new webpage and want to perform the same AI operation — say, checking nutritional information on a recipe page or comparing product specs across tabs — you have to re-enter the same prompt from scratch. This isn’t just tedious; it’s a signal that browser...

In this tutorial, we build a complete and practical Crawl4AI workflow and explore how modern web crawling goes far beyond simply downloading page HTML. We set up the full environment, configure browser behavior, and work through essential capabilities such as basic crawling, markdown generation, structured CSS-based extraction, JavaScript execution, session handling, screenshots, link analysis, concurrent crawling, and deep multi-page exploration. We also examine how Crawl4AI can be extended with LLM-based extraction to transform raw web content into structured, usable data. Throughout the tutorial, we focus on hands-on implementation to understand the major features of Crawl4AI v0.8.x and learn how to apply them to realistic data extraction and web automation tasks. Copy Code Copied Use a different Browser import subprocess import sys print(" Installing system dependencies...") subprocess.run(['apt-get', 'update', '-qq'], capture_output...