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#Product updates

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Nov 14

Saturday
  1. Advancing science & engineering with Antigravity’s /teamwork

    Over the past several weeks, teams across Google and academic partner institutions have tackled open problems in math, science, and complex systems engineering that had remained open for years. We are making these agentic tools available in Antigravity’s /teamwork (alongside /boost and other agents) so that researchers and developers can apply them to their own work.

Nov 13

Friday
  1. How Natera designed an agentic AI system for lab ops with Amazon Bedrock AgentCore

    Natera built SWARM, an agentic AI system on Amazon Bedrock AgentCore that captures institutional lab-operations knowledge and deploys it at scale. This post walks through the full architecture, from knowledge base construction to deployment and evaluation, delivering about 130x faster first response and 96.2% automated coverage across support tickets.

Nov 12

Thursday

Nov 11

Wednesday
  1. NVIDIA Expands Open Source Commitment With OpenShell Contribution and New AI Infrastructure

    Kubernetes, the open source system that manages how applications run across thousands of AI factories, has become the foundation for AI, and the demands on it are only growing. To help meet them, NVIDIA is deepening its commitment to open source at KubeCon + CloudNativeCon, the flagship conference for the Kubernetes and cloud-native communities.

Nov 10

Tuesday
  1. How skills and agents work in AWS Quick

    Repeatable tasks like status updates, release notes, and ticket creation can be encoded in AWS Quick through agents and skills. This post covers how agents, skills, and your data fit together in AWS Quick, how to build and share skills for your team, and how the official catalog and custom agents help you use them.

Nov 9

Monday

Nov 7

Saturday
  1. SageMaker Inference now supports Multi-Instance GPU to maximize GPU utilization for model endpoints

    Amazon SageMaker Inference now supports GPU partitioning with NVIDIA Multi-Instance GPU (MIG), which divides one GPU into hardware-isolated partitions so you can run a different model in each or take a right-sized fraction of a newer accelerator. This post shows how two optional API fields place one model per partition, and what we measured against a whole older GPU.

Nov 6

Friday