AI

This post was co-written with Daniel Han and Michael Han from Unsloth. Deploying large foundation models (FMs) stored at their original 16-bit floating-point precision (BF16 or FP16) is expensive. They need large GPU instances, driving up serving costs, and slowing down iteration cycles. Quantization addresses this by reducing the numerical precision of a model’s weights […]

In this post, learn how KTern.AI, an SAP digital transformation platform, used Amazon Bedrock AgentCore to build and deploy AI agents ready for enterprise-scale SAP transformation workloads. These agents autonomously orchestrate workflows from reverse engineering, fit-to-standard, and code analysis to exception mining in Finance and Sales processes. The result is automation without custom agent infrastructure.

When prefill and decode share a GPU, long prompts stall token generation for every concurrent request. Disaggregated Prefill and Decode (DPD) removes this interference by running each phase on separate GPU pools connected through Elastic Fabric Adapter (EFA) with Remote Direct Memory Access (RDMA). Large language model (LLM) inference has two fundamentally different phases. Prefill

Billed as the “world’s first museum of AI arts,” Dataland uses wearables and troves of material from the Amazon to merge nature, biometrics, and art.

Amid live coding sessions and Silicon Valley optimism, the UN’s AI for Good summit wrestled with an increasingly urgent question: Can global governance catch up before the technology races beyond its control?

The move comes after Simo took significant medical leave. Simo will stay on as a part-time advisor.

Claude subscribers must soon pay usage-based fees to access Anthropic’s best consumer AI model—a sign that the golden era of AI subscriptions is ending.

When Model Context Protocol (MCP) tools underperform, the cause is rarely the protocol itself but the tool design. Many teams start by exposing an existing API as-is and trusting the agent to figure out the rest. It is a natural way to extend APIs to agentic systems and generative AI coding tools. For straightforward use

As enterprises scale their generative AI workloads, the demand for faster, more observable, and more flexible inference infrastructure continues to grow. Amazon SageMaker HyperPod is rising to meet that challenge with a set of new capabilities designed to streamline how organizations deploy and operate large models in production. Teams can now record inputs and outputs

The soft, weirdly sexualized home-chore robot has been given some very tactile hands.