Cloud Computing

Live sports broadcasting represents the ultimate stress test for digital media infrastructure, where operational success or failure is measured in milliseconds and observed live by millions of viewers simultaneously. During the 2026 FIFA World Cup, the stakes reached a high for TelevisaUnivision, the leading Spanish-language media conglomerate. With Mexico serving as both a primary host […]

In the modern data landscape, query performance tuning and managing system price-performance is challenging, especially as the number of agentic workloads increase. Even for experienced developers and DBAs, constantly analyzing query execution plans, tweaking schemas, and adding query hints with ever exploding volume, variety, and velocity of data is a never-ending cycle that drains business

For enterprises and governments with strict compliance and sovereignty requirements, keeping sensitive data on-premises often means missing out on the latest AI. These organizations are managing three major risks: Jurisdictional risk: Shifting local regulations, the need to protect intellectual property and the potential of foreign data access requests make local data handling essential. Economic independence:

I’ve talked to developers, IT leaders, and builders who all ask the same question: How do we actually get agents into production?  The answer isn’t theoretical — it’s hands-on. Whether it’s designing a system that allows your agents to interact with external data sources while maintaining strict security guardrails or creating self-optimizing supply chain workflows

Written by: Tyler McLellan, Austin Larsen Introduction Google Threat Intelligence Group (GTIG) continues to track UNC6671 actively conducting compromises leading to data theft extortion, despite the alleged announced retirement of the BlackFile extortion brand in May 2026. Telemetry and infrastructure analysis reveal that rather than disbanding, UNC6671 has diversified its operations across multiple extortion fronts

Nearly every major AI lab uses Google Cloud infrastructure, including for training of models, inference for agents, and new frontier research. Google Cloud also continues to be the platform of choice for new, high-growth AI startups who are driving much of the industry’s research and innovation. Today, we’re announcing that Mirendil, an exciting frontier AI

Amazon Bedrock AgentCore is a managed service for running AI agents in production. You bring your agent code packaged as a container, and Bedrock AgentCore handles the operational concerns: scaling, session routing, health checking, and providing managed capabilities like conversation memory. AWS.AgentCore.Hosting is the .NET library that connects your agent code to this service. It