GovTech Singapore and Google DeepMind are developing on-device AI models based on Gemma to detect conversational scams without cloud data transmission.
Google DeepMind has launched EmbeddingGemma 2, a modular, multimodal model capable of mapping text, images, video, and audio into a single vector space for local device execution.
Google has introduced Google AI Edge Foresight, an offline-first Mac application for meeting notes that utilizes the on-device EmbeddingGemma 2 model.
Google has launched EmbeddingGemma 2, an open-source, multimodal AI model that enables local semantic search across various media types on consumer devices.
Google DeepMind has released EmbeddingGemma 2, a multimodal embedding model built on Gemma 4 that supports text, image, and audio processing with a 8,192-token context window.
Google DeepMind has launched EmbeddingGemma 2, an open-weight multimodal model designed for on-device use with 740 million parameters and commercial-friendly licensing.
Google DeepMind has introduced the Gemma 4 12B model, which features a unified architecture that eliminates the need for traditional media-specific encoders.
An experiment connecting local Gemma models to Codex via MCP revealed that delegating routine tasks to a local LLM can double cloud token consumption instead of reducing it.
An Android developer built an AI-agent application using AI-assisted coding, resulting in 1,600 commits and a graph-based architecture running local models.
Google Research has launched ToolGrad, an answer-first framework for tool-use data generation that achieves a 99.8% pass rate by prioritizing verified API execution over query-first search.
