olmo-eval is a new evaluation workbench designed to support the iterative evaluation cycle during LLM development. Built on the OLMES standard, it offers flexible task definitions, swappable runtime policies, and detailed per-question comparison to help developers determine whether interventions are significant.

Learn how to choose a secure sandbox for AI agents, with guidance on filesystem isolation, network access, resource limits, and microVMs.

This post shows how to build a custom meeting prep and follow-up assistant using Amazon Quick and Cisco Webex MCP servers. From a single prompt, the agent finds an upcoming Webex meeting, reviews prior meeting summaries and transcripts, and pulls related Vidcast highlights and transcript context. It then searches Webex message threads for unresolved follow-ups and creates a concise prep brief. After the meeting, the same assistant can summarize the discussion and identify action items. It can also find related Vidcast updates and draft a follow-up message for the right Webex space.

This post outlines a cost-effective and scalable intelligent document processing pipeline on AWS using Amazon Bedrock, including its Data Automation (BDA) for extraction, Strands Agent for coordination, and Knowledge Bases for contextual understanding across documents.

AWS Professional Services compressed engagement timelines from months to days by fundamentally rebuilding its delivery process, not just adding AI tools. This post shares how they became a frontier team and the practices that enabled it.

Databricks introduces Lakehouse Federation to connect to external data sources without migration, allowing natural language queries via Genie while maintaining governance through Unity Catalog.