Category: Amazon Nova

New fast, cost-effective model supports extended thinking with adjustable reasoning depth, letting you control the balance between speed, intelligence, and cost while building AI applications for everyday workloads.

New program gives organizations unprecedented access to Nova model training, enabling them to build custom frontier models that deeply embed domain expertise without the traditional barriers of cost, compute, and time.

Build more natural voice interactions with enhanced speech-to-speech AI—now featuring multilingual conversations, dynamic speech control, crossmodal inputs, and improved telephony integration while maintaining conversation context across tasks.

New AWS service helps developers build AI agents that automate browser-based tasks like form filling, search & extract, shopping & booking, and QA testing—with over 90% reliability for enterprise deployments.

Last week I met Jeff Barr at the AWS Shenzhen Community Day. Jeff shared stories about how builders around the world are experimenting with generative AI and encouraged local developers to keep pushing ideas into real prototypes. Many attendees stayed after the sessions to discuss model grounding, evaluation, and how to bring generative AI into […]

AWS announced Amazon Nova Web Grounding, a new built-in tool for Nova models on Amazon Bedrock that automatically retrieves current, cited information to reduce AI hallucinations and improve accuracy in applications requiring up-to-date factual data.

The Nova Act extension is a new IDE-integrated tool that enables developers to create browser automation agents using natural language through the Nova Act model, offering features like Builder Mode, chat capabilities, and predefined templates while streamlining the development process without leaving their preferred development environment.

AWS now enables extensive customization of Amazon Nova foundation models through SageMaker AI with techniques including continued pre-training, supervised fine-tuning, direct preference optimization, reinforcement learning from human feedback and model distillation to better address domain-specific requirements across industries.