AI

Dating apps and AI companies have been touting bot wingmen for months. But the future might just be good old-fashioned meet-cutes.

US support for nuclear energy is soaring. Meanwhile, coal plants are on their way out and electricity-sucking data centers are meeting huge pushback. Welcome to the next front in the energy battle.

Operating a self-managed MLflow tracking server comes with administrative overhead, including server maintenance and resource scaling. As teams scale their ML experimentation, efficiently managing resources during peak usage and idle periods is a challenge. Organizations running MLflow on Amazon EC2 or on-premises can optimize costs and engineering resources by using Amazon SageMaker AI with serverless

Businesses face a growing challenge: customers need answers fast, but support teams are overwhelmed. Support documentation like product manuals and knowledge base articles typically require users to search through hundreds of pages, and support agents often run 20–30 customer queries per day to locate specific information. This post demonstrates how to solve this challenge by

Google’s AI is now even smarter, and more versatile.

The battle for AI dominance has left a large footprint—and it’s only getting bigger and more expensive.

In the AI boom, chatbots and GPTs come and go quickly. (Remember Llama?) GPT-5 had a big year, but 2026 will be all about Qwen.

Intelligent Document Processing (IDP) transforms how organizations handle unstructured document data, enabling automatic extraction of valuable information from invoices, contracts, and reports. Today, we explore how to programmatically create an IDP solution that uses Strands SDK, Amazon Bedrock AgentCore, Amazon Bedrock Knowledge Base, and Bedrock Data Automation (BDA). This solution is provided through a Jupyter notebook that enables users

Enterprise organizations increasingly rely on web-based applications for critical business processes, yet many workflows remain manually intensive, creating operational inefficiencies and compliance risks. Despite significant technology investments, knowledge workers routinely navigate between eight to twelve different web applications during standard workflows, constantly switching contexts and manually transferring information between systems. Data entry and validation tasks