Release of llm-anthropic 0.25.1 adds support for Claude Opus 4.8, fast mode option for eligible accounts, and changes default max_tokens to each model's maximum output.

Azercell Telecom collaborated with the AWS Generative AI Innovation Center to build an Azerbaijani LLM on Amazon SageMaker AI, achieving 23% higher training throughput, 58% lower peak GPU memory, and 2× token efficiency via custom tokenizer, FSDP, and Liger Kernel optimizations.

At Google I/O 2026, Google Research showcased breakthroughs in scientific discovery, health, edge computing, and weather prediction. Highlights include Gemini for Science (ERA, Co-Scientist), Google Health app, Symptom AI, AMIE, Coral NPU, and AI for extreme weather. These innovations demonstrate AI's potential to amplify human ingenuity.

Learn how to build a custom portal embedding SageMaker AI MLflow Apps UI using a React frontend and Flask reverse proxy with AWS SigV4 authentication, deployed via AWS CDK. This solution provides a persistent, bookmarkable URL for MLflow without requiring presigned URLs or AWS Console access.

This post demonstrates how to build a secure Flask-based MLflow proxy service that provides HTTPS access to Amazon SageMaker MLflow without requiring the MLflow SDK. This solution is for organizations undergoing cloud transformation who want to preserve their existing ML workflows while adopting cloud-native services.

This post combines learnings from LangChain’s work on evaluating deep agents and Anthropic’s guide to demystifying evals for AI agents into a practical guide. You will learn how to apply five evaluation patterns for deep agents, build offline evaluations using pytest and LangSmith, and configure online monitoring for production. The walkthrough uses a text-to-SQL deep agent with Amazon Bedrock for the full development to production lifecycle.