Google is rolling out (in GA) a new Gemini model, 1.5 Flash-8B, our smallest production model with SOTA performance for its size.

Sourcegraph's Cody AI assistant, integrated with Google's Gemini 1.5 Flash, can evaluate the advantages of using long-context windows in AI models for code generation and understanding.

Today, we're announcing that Francois Chollet, the creator of Keras and a leading figure in the AI world, is embarking on a new chapter in his career outside of Google.

The first Web AI Summit, hosted by Google on October 18, 2024, brought together experts in machine learning models for web browsers.

vLLM's continuous batching and Dataflow's model manager optimizes LLM serving and simplifies the deployment process, delivering a powerful combination for developers to build high-performance LLM inference pipelines more efficiently.

Grounding with Google Search for the Gemini API and Google AI Studio enhances the accuracy and freshness of Gemini's responses by leveraging Google Search data.

Google is building AI models, focusing on Gemma, to bridge communication gaps across languages.

Google is committed to investing in privacy-enhancing technologies (PETs) to ensure user data privacy, improving products like Google Home and Google Search.

Chrome on Android will soon natively support third-party autofill services, providing a smoother user experience.

Firebase Demo Day is back! Join us virtually on November 19, 2024 for a showcase of cutting-edge Firebase technology.

KerasHub is a new unified library for pretrained models fostering a more cohesive ecosystem for developers.

The season showcases new applications of Gemma, including a personal AI code assistant and projects for non-English tasks and business email processing.

Developers can now access and build with the latest Gemini models through the OpenAI Library and REST API. Update your code with three lines and get started.

Compare Mode is a powerful new feature designed to help you make informed decisions about which Gemini model best suits your needs.

AgentOps uses the Gemini API to provide cost-effective and powerful LLM-powered agent observability for enterprises.

The integration of Gemini's 1.5 models with Sublayer's Ruby-based AI agent framework enables developer teams to automate their documentation process, streamline workflows, and build AI-driven applications.