Category: FAANG

By Roberto Perez Alcolea, Java Platform Team Quasar connects a proposed API change with observed references in consumer repositories, giving the producer evidence for a release decision At Netflix, shared Java libraries support applications developed across a large, independently evolving repository ecosystem. A library change begins in one repository, but its impact can reach many others. Producers …

Read more “Beyond API Compatibility: Building a Tool to Measure the Blast Radius of Library Changes”

Most fine-tuning teaches a model to produce a better answer to a single prompt. Agentic tasks are different. An agent that files an expense report or triages a support ticket takes a sequence of actions, and each step depends on what came before. There’s rarely one correct sequence to imitate, and the only reliable feedback …

Read more “Multi-Turn Reinforcement Learning on Amazon SageMaker AI: A hands-on guide”

Samartha Chandrashekar & Nick Parker You click play on a Friday night, and in the second that follows, dozens of services answer in milliseconds. Most of them run on Titus, our internal container platform. Titus is, in essence, a Kubernetes-based container hosting platform: we keep both the control plane and the data plane (the container hosts …

Read more “Single-tenant Titus: VM-level isolation on a multi-tenant container platform”

Amazon Polly Generative engine now offers more natural and accurate speech output. We’re introducing an upgrade to the Amazon Polly Generative engine that delivers more expressive, more accurate speech output with low latency. The upgrade adds 14 new voices across 9 locales, including general availability of Generative voices in Hindi and Finnish for the first …

Read more “Amazon Polly delivers a major upgrade to its Generative engine”

When enterprise data sits behind a corporate firewall or in a private on-premises data center, there’s no direct network path from AWS, even though the AI agents and dashboards in Amazon Quick can query virtually any data source. Administrators must make sure their sensitive enterprise data isn’t exposed to the public internet while being accessible …

Read more “Securing Quick access from corporate networks with AWS PrivateLink”

Most large language models (LLMs) use transformer architectures with softmax attention. During inference, their key-value (KV) cache grows with the conversation or document context. This can reduce concurrency, increase serving cost, and force agentic applications to discard useful history. i14 designed Laplace 3B as a linear attention alternative. Its recurrent state stays at 29.5 MB …

Read more “Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch”

Amazon Bedrock now supports structured outputs for all Anthropic Claude models, on both the bedrock-runtime and bedrock-mantle endpoints, where the model is available. Whether you call Claude through Boto3 and the bedrock-runtime APIs or through the Messages API and the bedrock-mantle endpoint, you can now receive schema-compliant JSON responses that conform to a defined structure. …

Read more “Structured outputs for Anthropic Claude models on Amazon Bedrock”