Learnings from COBOL modernization in the real world
AWS shared learnings from real-world COBOL modernization projects. Successful modernization requires both reverse engineering to create specs and forward engineering with AI coding assistants.
Why this matters: Offers practical guidance for organizations modernizing legacy systems, a common challenge in enterprise IT.
Reinforcement fine-tuning for Amazon Nova: Teaching AI through feedback
AWS describes reinforcement fine-tuning for Amazon Nova models, which customizes AI through feedback rather than imitation. The post covers applications from code generation to customer service and implementation options.
Why this matters: This technique could help enterprises better tailor AI models to specific business needs without extensive labeled data.
Pacific Northwest National Laboratory and OpenAI partner to accelerate federal permitting
Pacific Northwest National Laboratory and OpenAI introduced DraftNEPABench, a benchmark for AI coding agents in federal permitting. The tool shows potential to reduce NEPA drafting time by up to 15%.
Why this matters: Demonstrates how AI could accelerate infrastructure project reviews by automating regulatory documentation.
OpenAI Codex and Figma launch seamless code-to-design experience
OpenAI Codex and Figma launched an integration connecting code and design workflows. The tool aims to help teams iterate and ship products faster.
Why this matters: Streamlines collaboration between developers and designers, potentially reducing development cycles.
Efficiently serve dozens of fine-tuned models with vLLM on Amazon SageMaker AI and Amazon Bedrock
AWS demonstrates how to efficiently serve multiple fine-tuned models using vLLM on Amazon SageMaker and Amazon Bedrock. The implementation includes kernel-level optimizations for Mixture of Experts models.
Why this matters: This enables enterprises to deploy and manage multiple specialized AI models cost-effectively while maintaining performance.
Building intelligent event agents using Amazon Bedrock AgentCore and Amazon Bedrock Knowledge Bases
AWS shows how to build intelligent event agents using Amazon Bedrock AgentCore and Knowledge Bases. The system maintains attendee preferences and provides personalized experiences.
Why this matters: This provides a template for organizations to create scalable, personalized AI assistants without extensive custom infrastructure development.
Train CodeFu-7B with veRL and Ray on Amazon SageMaker Training jobs
AWS published a technical guide for training the CodeFu-7B model using specific reinforcement learning methods. The process utilizes distributed computing on SageMaker.
Why this matters: It provides a replicable framework for organizations to train specialized, large-scale AI models efficiently.
Generate structured output from LLMs with Dottxt Outlines in AWS
An AWS blog post details a method for generating structured outputs from large language models. The approach uses the Dottxt Outlines framework within SageMaker.
Why this matters: This enables more reliable integration of LLMs into applications that require consistent data formats.
Global cross-Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan
AWS has expanded global cross-region inference for Anthropic's Claude AI models to five Southeast Asian countries. The announcement includes technical implementation guidance and quota management best practices.
Why this matters: Enables enterprises in these regions to deploy Claude models with improved resilience and lower latency for AI applications.
Introducing Amazon Bedrock global cross-Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)
AWS has launched global cross-region inference for Anthropic's Claude AI models in the UAE and Bahrain. The post details model capabilities, resilience benefits, and includes implementation code.
Why this matters: Allows Middle Eastern businesses to build generative AI applications with enhanced performance and reliability.
Scaling data annotation using vision-language models to power physical AI systems
Bedrock Robotics uses vision-language models to analyze construction footage and generate labeled datasets for autonomous equipment training. This collaboration with AWS aims to scale data annotation.
Why this matters: Automates labor-intensive data preparation for physical AI systems in industrial settings.
How Sonrai uses Amazon SageMaker AI to accelerate precision medicine trials
Sonrai implemented an MLOps framework using Amazon SageMaker AI for precision medicine trials. The system maintains traceability and reproducibility required in regulated healthcare environments.
Why this matters: Enables compliant AI deployment in regulated clinical trial settings.
Accelerating AI model production at Hexagon with Amazon SageMaker HyperPod
Hexagon scaled AI model production by pretraining segmentation models using Amazon SageMaker HyperPod infrastructure. This collaboration with AWS accelerated their model development pipeline.
Why this matters: Reduces infrastructure management overhead for enterprise AI model training.
OpenAI announces Frontier Alliance Partners
OpenAI launched Frontier Alliance Partners to help enterprises transition AI projects from pilots to production deployments.
Why this matters: Addresses the common challenge of scaling AI implementations from experimental to operational stages.
Amazon SageMaker AI in 2025, a year in review part 1: Flexible Training Plans and improvements to price performance for inference workloads
Amazon SageMaker AI introduced Flexible Training Plans and improved price performance for inference workloads in 2025. These were part of broader infrastructure enhancements.
Why this matters: These improvements help organizations manage AI training costs and optimize deployment efficiency.
Amazon SageMaker AI in 2025, a year in review part 2: Improved observability and enhanced features for SageMaker AI model customization and hosting
Amazon SageMaker AI enhanced observability, model customization, and hosting capabilities in 2025. These updates followed earlier infrastructure improvements.
Why this matters: Better observability and customization tools enable more sophisticated AI deployment and monitoring.
Integrate external tools with Amazon Quick Agents using Model Context Protocol (MCP)
AWS provides a six-step checklist for building or validating MCP servers to integrate external tools with Amazon Quick Agents. This guide details implementation requirements for third-party partners.
Why this matters: Enables developers to extend Amazon Quick's capabilities by connecting specialized tools through standardized protocols.
GGML and llama.cpp join HF to ensure the long-term progress of Local AI
GGML and llama.cpp have partnered with Hugging Face to promote the development of Local AI technologies.
Why this matters: This collaboration aims to enhance the accessibility and effectiveness of AI solutions in local environments.
Build AI workflows on Amazon EKS with Union.ai and Flyte
AWS detailed how to orchestrate AI workflows using Flyte on Amazon EKS, integrating with AWS services including S3 Vectors.
Why this matters: Provides enterprises with a scalable method to deploy and manage complex AI pipelines in cloud environments.
Amazon Quick now supports key pair authentication to Snowflake data source
Amazon Quick Sight now supports key pair authentication for connecting to Snowflake data sources.
Why this matters: Enhances security for business intelligence tools accessing sensitive data in cloud data warehouses.