AI

This week on “Uncanny Valley,” we discuss the voluntary AI safety agreement tech executives signed, AI agents for normies, and extremist candidates running for office in the US midterms.

Quick Apps could already bring live data into an app from connectors and content sources: action connectors (services like Jira, Slack, and Google Drive), Spaces documents, web search, and AI inference all run at view time, not build time. The solution introduces live structured data from your data lakes, databases, and other analytics data stores.

In my previous post: Building persistent memory for multi-agent AI systems with Amazon S3 Vectors, we explored why memory engineering is the foundational discipline for production multi-agent systems. We showed how Amazon S3 Vectors, a capability of Amazon Simple Storage Service (Amazon S3), meets the architectural requirements for agent memory: semantic retrieval, rich metadata, strong

Generative AI has made it possible to produce large amounts of personalized content quickly and at low cost. In our previous post, we showed how generative AI on Amazon Bedrock can produce personalized content at scale while staying within brand guidelines and guardrails. The new challenge is now one of selection. Among all of those

Teams that process documents at scale know the routine: files land in storage, someone notices, opens each one, decides what it needs, and routes it for review. Monitoring alerts queue up the same way, waiting for a person to act on them. The hours lost to manual triage are the operational problem ambient agents solve.

You need Claude Platform on AWS (CPonAWS) inference from three environments: production workloads on AWS, developer laptops for local iteration, and external services on other cloud providers or on-premises continuous integration and continuous delivery (CI/CD) pipelines. Each environment has different authentication requirements, but all should share a single subscription with workspace-level isolation between production and

Amazon Quick Sight is a fully managed, cloud-native business intelligence (BI) capability for building and publishing interactive dashboards. You can access these dashboards from any device and embed them into your applications, portals, and websites. When building a dashboard, authors add filters so readers can narrow the data to what they want to analyze. But

uniopen is a digital communication and membership platform launched by Taiwan’s Uni-President Enterprises Group, connecting customers to ecommerce, membership benefits, and other retail experiences across web, tablet, and mobile channels. Across those channels, uniopen applies a moderation policy that classifies each interaction along two axes. The first is what behavior occurred (nine categories), and the

Six major AI companies signed a voluntary agreement with the White House this week vowing to implement safeguards. It’s unclear how much that matters.

OpenAI’s Dots and Meta’s Muse are vying to be your AI agent of choice. I’ve tried both—and I suspect you will too.