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Google's Gemini Live now offers real-time visual guidance, highlighting objects in users' camera feeds and integrating more deeply with apps, transforming how AI assistants handle day-to-day tasks.
Google has announced a set of significant updates for Gemini Live, its AI assistant designed for real-time conversations. The latest features enhance the assistant's capabilities by adding visual guidance and deeper app integration, making it more useful in day-to-day tasks.
The primary technical update is the introduction of real-time visual guidance. This feature allows Gemini Live to highlight objects directly on your screen while you share your camera feed. Here’s a breakdown of how this works:
This feature is particularly useful for tasks like finding the right tool in a toolbox or identifying a component in a complex setup. For example, if you’re working on a DIY project and need to find a specific screwdriver, you can point your smartphone’s camera at your tools, and Gemini Live will highlight the correct one.
In addition to visual guidance, Gemini Live is gaining the ability to interact with other apps on your device. This includes:
These interactions are facilitated through deep integration with Android’s app ecosystem. Google has likely implemented APIs that allow Gemini Live to communicate with these apps securely and efficiently.

The visual guidance feature will be available on the newly announced Pixel 10 devices when they launch on August 28th. This ensures that users of the latest Pixel models can take advantage of these advanced AI capabilities right out of the box.
For developers and tech enthusiasts, these updates highlight several key points:
To achieve these features, Google likely employed:
Google’s updates to Gemini Live represent a significant step forward in AI assistant technology. By combining visual guidance with app interaction, the assistant becomes a more versatile and practical tool for users. As these technologies continue to evolve, we can expect even more innovative applications in the future.
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About the author
Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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26 August 2025
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