engblogs

A study shows that repeating scarce target-domain data in mixture pretraining yields better target-domain performance under data constraints and introduces a repetition-aware scaling law to optimize data mixtures.

A data-efficient approach to multilingual knowledge transfer that applies lexical substitutions in high-resource pretraining data via a bilingual vocabulary to help low-resource languages reach parity without extra model training.

A practical guide to adding context-aware security monitoring to FHIR APIs with Amazon Bedrock, enabling anomaly detection, automated data sensitivity classification, and natural-language compliance reporting without impacting API latency.

AWS vector solutions enable agentic AI by keeping data where it lives and delivering fast, contextual retrieval through vectors across OpenSearch, S3 Vectors, DynamoDB, Neptune, and other services, powering RAG, semantic search, and knowledge graphs without data migration.

Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore, a four-agent framework (Intake, IaC, Migration Intelligence & Governance, SRE) that automates discovery, infrastructure-as-code generation, portfolio governance, and proactive post-migration operations at scale with built-in security and human-in-the-loop oversight.

Part 2 of a series on scaling agentic AI in enterprises, detailing framework-agnostic patterns to manage multi-everything complexity while preserving flexibility and avoiding vendor lock-in via a unified control plane and platform.

Converting natural-language policy documents into Dogwood rules and enforcing them in real time across Amazon Bedrock AgentCore with Policy Authoring, Guardrails, and temporal, prerequisite, and rate-limiting controls.

Replit's Free Mode, powered by GPT-5.6 Luna, lowers costs and preserves project context to democratize software creation at scale for users of all technical backgrounds.

Explains how Zero Data Retention and Private Safety Processing enable privacy-preserving safety monitoring for frontier models by keeping customer content on customer-controlled infrastructure or encrypted with customer-managed keys, while emitting limited signals to detect misuse across related interactions.

Explores how Replit's Free Mode powered by GPT-5.6 Luna lowers the cost of software creation, broadens access to millions of users, and demonstrates how price performance and model economics enable practical AI-assisted development.

Examines Zero Data Retention and Private Safety Processing for frontier models, detailing how customer content remains private across interactions, how encrypted storage and customer-controlled keys protect data, and how cross-interaction safety signals enable risk detection without exposing content to OpenAI personnel.

Free Mode powered by GPT-5.6 Luna accelerates turning ideas into working software at scale by reducing costs through improved model economics, enabling context-aware agents and a seamless path from ideation to Build Mode.