I/O 2026
At Google I/O 2026, we shared how we’re making AI more helpful for everyone. See everything we announced.
A new era for AI Search
We shared the next step in our journey to bring together the best of a search engine with the best of AI.
Gemini 3.5: frontier intelligence with action
At Google I/O we released Gemini 3.5, our latest series of models combining frontier intelligence with action.
Everything new in our Google AI subscriptions, fresh from I/O 2026
Introducing a $100 AI Ultra plan — plus, new features and benefits for Google AI Plus, Pro and Ultra subscribers.
How AI Mode is changing the way people search in the U.S.
One year after launch, see how AI Mode’s users are shifting from keywords to natural language queries.
New ways to create and get things done in Google Workspace
Announcing new voice capabilities in Gmail, Docs and Keep, a new design tool called Google Pics and updates to AI Inbox.
I/O 2026: Welcome to the agentic Gemini era
The latest from Google I/O: See how we’re helping you get more done with Gemini.
Scalable voice agent design with Amazon Nova Sonic: multi-agent, tools, and session segmentation
Design patterns for scalable voice agents matter for organizations that need to deliver fast, natural, and reliable voice experiences. Many teams face challenges like high latency, managing real-time audio, and coordinating multiple agents in complex workflows. In this post, you’ll learn how to use Amazon Nova Sonic, Amazon Bedrock AgentCore, and Strands BidiAgent to build …
Extending conversational memory in Kiro CLI using Amazon Bedrock AgentCore Memory
Agentic IDEs that forget what you told them in previous sessions aren’t very helpful. You work on your large codebase with complex business requirements for days or weeks. However, your IDE only remembers you during your current session and can’t recall your conversational history, preferences derived from the conversations, or additional insights. You end up …
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Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. It now supports Apache Iceberg table format, streaming ingestion, scalable batch ingestion, and fine-grained access control through AWS Lake Formation. As organizations scale their machine learning platforms from experimentation to production, two operational challenges …
Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store Read More »