50 results
SEPT. 24, 2025 / Mobile
Google AI Edge provides the tools to run AI features on-device, and its new LiteRT-LM runtime is a significant leap forward for generative AI. LiteRT-LM is an open-source C++ API, cross-platform compatibility, and hardware acceleration designed to efficiently run large language models like Gemma and Gemini Nano across a vast range of hardware. Its key innovation is a flexible, modular architecture that can scale to power complex, multi-task features in Chrome and Chromebook Plus, while also being lean enough for resource-constrained devices like the Pixel Watch. This versatility is already enabling a new wave of on-device generative AI, bringing capabilities like WebAI and smart replies to users.
SEPT. 10, 2025 / AI
We are launching 1.0 stable release of Genkit Go, empowering Go developers to build performant, production-ready AI-powered applications with Genkit. Recent enhancements include support for integrating and building MCP tools, expanding third-party model provider support, and production AI monitoring with Firebase. Additionally, we are announcing a new feature in the Genkit CLI to provide AI development tools, like the Gemini CLI and Cursor, with the latest knowledge of Genkit - supercharging Genkit development experience when using AI assistance.
SEPT. 4, 2025 / Gemma
Introducing EmbeddingGemma: a new embedding model designed for efficient on-device AI applications from Google. This open model is the highest-ranking text-only multilingual embedding model under 500M parameters on the MTEB benchmark, enabling powerful features like RAG and semantic search directly on mobile devices without an internet connection.
AUG. 28, 2025 / AI
Detailed prompting techniques and best practices for various applications, including photorealistic scenes, stylized illustrations, product mockups, and more using Google's newly released Gemini 2.5 Flash Image; a natively multimodal model capable of generating, editing, and composing images using text, supporting capabilities like text-to-image, image editing, style transfer, and multi-image composition.
JULY 30, 2025 / Gemini
LangExtract is a new open-source Python library powered by Gemini models for extracting structured information from unstructured text, offering precise source grounding, reliable structured outputs using controlled generation, optimized long-context extraction, interactive visualization, and flexible LLM backend support.
JULY 22, 2025 / Gemini
Gemini 2.5 Flash-Lite, previously in preview, is now stable and generally available. This cost-efficient model is ~1.5x faster than 2.0 Flash-Lite and 2.0 Flash, offers high quality, and includes 2.5 family features like a 1 million-token context window and multimodality.
JULY 16, 2025 / Gemini
The updated Agent Development Kit (ADK) simplifies and accelerates the process of building AI agents by providing the CLI with a deep, cost-effective understanding of the ADK framework, allowing developers to quickly ideate, generate, test, and improve functional agents through conversational prompts, eliminating friction and keeping them in a productive "flow" state.
JULY 16, 2025 / AI
The `logprobs` feature has been officially introduced in the Gemini API on Vertex AI, provides insight into the model's decision-making by showing probability scores for chosen and alternative tokens. This step-by-step guide will walk you through how to enable and interpret this feature and apply it to powerful use cases such as confident classification, dynamic autocomplete, and quantitative RAG evaluation.
JULY 10, 2025 / Cloud
Updates in Firebase Studio include new Agent modes, foundational support for the Model Context Protocol (MCP), and Gemini CLI integration, all designed to redefine AI-assisted development allow developers to create full-stack applications from a single prompt and integrate powerful AI capabilities directly into their workflow.
JUNE 25, 2025 / Gemini
A research prototype simulating a neural operating system generates UI in real-time adapting to user interactions with Gemini 2.5 Flash-Lite, using interaction tracing for contextual awareness, streaming the UI for responsiveness, and achieving statefulness with an in-memory UI graph.
