Google code homepage
Announcing Imagen 4 Fast and the general availability of the Imagen 4 family in the Gemini API
Google announces the general availability of Imagen 4, its advanced text-to-image model, in the Gemini API and Google AI Studio, featuring significant improvements in text rendering. The new Imagen 4 Fast model, designed for speed and rapid image generation, is now available alongside Imagen 4 and Imagen 4 Ultra, with Imagen 4 and Imagen 4 Ultra also supporting up to 2K resolution image generation.
The agentic experience: Is MCP the right tool for your AI future?
Apigee helps enterprises integrate large language models (LLMs) into existing API ecosystems securely and scalably, addressing challenges like authentication and authorization not fully covered by the evolving Model Context Protocol (MCP), and offering an open-source MCP server example that demonstrates how to implement enterprise-ready API security for AI agents.
Simplify your Agent "vibe building" flow with ADK and Gemini CLI
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.
Stanford’s Marin foundation model: The first fully open model developed using JAX
The Marin project aims to expand the definition of 'open' in AI to include the entire scientific process, not just the model itself, by making the complete development journey accessible and reproducible. This effort, powered by the JAX framework and its Levanter tool, allows for deep scrutiny, trust in, and building upon foundation models, fostering a more transparent future for AI research.
Unlock Gemini’s reasoning: A step-by-step guide to logprobs on Vertex 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.
Build with Veo 3, now available in the Gemini API
Veo 3, Google’s latest AI video generation model, is now available in paid preview via the Gemini API and Google AI Studio. Unveiled at Google I/O 2025, Veo 3 can generate both video and synchronized audio, including dialogue, background sounds, and even animal noises. This model delivers realistic visuals, natural lighting, and physics, with accurate lip syncing and sound that matches on-screen action.
Conversational image segmentation with Gemini 2.5
Gemini's advanced capability for conversational image segmentation allows intuitive interaction with visual data by understanding complex phrases, conditional logic, and abstract concepts, streamlining developer experience and opening doors for new applications in media editing, safety monitoring, and damage assessment.
Gemini 2.5 Flash-Lite is now stable and generally available
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.
Unleashing new AI capabilities for popular frameworks in Firebase Studio
New AI capabilities for popular frameworks in Firebase Studio include AI-optimized templates, streamlined integration with Firebase backend services, and the ability to fork workspaces for experimentation and collaboration, making AI-assisted app development more intuitive and faster for developers worldwide.
Introducing Opal: describe, create, and share your AI mini-apps
Opal is a new experimental tool from Google Labs that helps you compose prompts into dynamic, multi-step mini-apps using natural language, removing the need for code, allowing users to build and deploy shareable AI apps with powerful features and seamless integration with existing Google tools.