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325 articles

325 ARTICLES

AWS Releases Loom, an Open-Source Reference Platform for Governing AI Agents at Enterprise Scale
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AWS Releases Loom, an Open-Source Reference Platform for Governing AI Agents at Enterprise Scale

AWS released Loom, an open-source reference platform on AWS Labs for governing AI agents at scale. Built on Strands Agents and Bedrock AgentCore Runtime, it implements RFC 8693 token exchange for identity propagation through delegated actor chains, config-driven deployments without runtime code generation, and mandatory tagging. AWS positions it as an example, not a managed service. By Steef-Jan Wiggers

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CW@60: The real promise of technology is human
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CW@60: The real promise of technology is human

The next chapter of technology will not be judged merely by the speed of its systems, but by its capacity to build trust, bridge societal divides and unlock human potential, says Dell’s Asia-Pacific chief

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How Netflix Built GenPage: a Single GenAI Model to Build Personalized Homepages
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How Netflix Built GenPage: a Single GenAI Model to Build Personalized Homepages

GenPage is a generative AI system developed by Netflix to replace its traditional multi-stage recommendation pipeline by directly generating personalized user homepages. GenPage leverages user history and request context as a prompt to produce the entire page, resulting in improved user engagement and reduced serving latency. By Sergio De Simone

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Google's AlphaEvolve Reaches General Availability with Evolutionary Code Optimization as a Service
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Google's AlphaEvolve Reaches General Availability with Evolutionary Code Optimization as a Service

Google’s AlphaEvolve reached general availability on the Gemini Enterprise Agent Platform, turning the DeepMind research project into an evolutionary code optimization service. Evaluators run client-side so code never leaves the customer’s infrastructure. Klarna doubled ML training throughput; practitioners note it only works where a measurable evaluation function exists. By Steef-Jan Wiggers

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Eclipse Dataspace Components on AWS: Cost optimization strategies
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Eclipse Dataspace Components on AWS: Cost optimization strategies

When you deploy Eclipse Dataspace Components (EDC) connectors on AWS, one of the first challenges you face is predicting and controlling the cost of the required infrastructure. Without clear benchmarks, it is difficult to make informed decisions about workload sizing, environment configuration, and long-term investment. Part 1 of this 3-part blog series covered the fundamentals

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Eclipse Dataspace Components on AWS: Architecture patterns in production
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Eclipse Dataspace Components on AWS: Architecture patterns in production

Running Eclipse Dataspace Components (EDC) connectors in production on AWS requires deliberate architecture decisions around isolation, managed services, and security layering. In Part 1 of this series, we covered the fundamentals of data space architectures and EDC per the International Data Space Association’s (IDSA) standards. If you are new to EDC, we recommend starting there.

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