AI

Anthropic’s newest foundation model, Claude Opus 4.5, is now available in Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models from leading AI companies. Opus 4.5 is a meaningful step forward in what AI systems can do and sets a new standard across coding, agents, computer use, and office tasks. […]

Amazon Bedrock Custom Model Import now supports OpenAI models with open weights, including GPT-OSS variants with 20-billion and 120-billion parameters. GPT-OSS models offer reasoning capabilities and can be used with OpenAI Chat Completions API. By preserving full OpenAI API compatibility, organizations can migrate their existing applications to AWS, gaining enterprise-grade security, scaling, and cost control.

Born out of an internal hackathon, Amazon’s Autonomous Threat Analysis system uses a variety of specialized AI agents to detect weaknesses and propose fixes to the company’s platforms.

The model policy team leads core parts of AI safety research, including how ChatGPT responds to users in crisis.

Azalia King was the last holdout preventing the construction of a Micron “megafab.” Onondaga County authorities threatened to use eminent domain to take her home away by force.

The Trump administration’s pressure on European regulators is having an impact, with fewer restrictions on Big Tech and canceled measures.

Recent findings reveal that even simple pricing algorithms can make things more expensive.

A message on OpenAI’s internal Slack claimed the activist in question had expressed interest in “causing physical harm to OpenAI employees.”

As organizations increasingly adopt AI capabilities across their applications, the need for centralized management, security, and cost control of AI model access is a required step in scaling AI solutions. The Generative AI Gateway on AWS guidance addresses these challenges by providing guidance for a unified gateway that supports multiple AI providers while offering comprehensive

Organizations have used geospatial machine learning (ML) for property risk assessment, disaster response, and infrastructure planning. These systems worked well but couldn’t scale beyond specialized use cases. Each question required multiple geospatial datasets, each with its own model and often its own workflow, limiting these capabilities to a handful of high-value use cases at the