Cloud Computing

ShinyHunters Renewed Mass Exploitation Campaign Targeting Oracle PeopleSoft

Cloud Computing /

Introduction  As an update to the June 2026 post, ShinyHunters Targets Education Sector with Oracle PeopleSoft Exploit, Mandiant and Google Threat Intelligence Group (GTIG) have identified renewed mass exploitation of CVE-2026-35273 by UNC6240 (ShinyHunters), along with expanded global targeting across multiple sectors. In June, the threat actor exploited this vulnerability as a zero-day predominantly against […]

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Agent Factory recap: Agent harnesses, shifting left, and autonomous coding

Cloud Computing /

In this episode of The Agent Factory, we explore the reality of building with autonomous agents alongside Ryan Lopopolo, a software engineer at Google Cloud and the person who coined the term agent harness. From throwing out manual code editors to treating team collaboration like leveling up RPG stats, Ryan breaks down how grounding models

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Google is a Leader in the 2026 Gartner Magic Quadrant for Container Management

Cloud Computing /

We’re excited and proud to share that Gartner has recognized Google as a Leader for the fourth year in a row in the 2026 Gartner® Magic Quadrant™ for Container Management, based on its Completeness of Vision and Ability to Execute. Google was positioned highest in Ability to Execute of all vendors evaluated and we believe

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Scribd, Inc. classifies more than 400 million documents with Gemini batch inference on Gemini Enterprise

Cloud Computing /

Scribd, Inc. is home to one of the world’s largest collections of human-created content.  Scribd’s  products leverage one of the world’s largest collections of human-created content and intelligent tools to help people move from information access to real understanding and application. This past year, Scribd used Gemini’s native PDF understanding and Gemini Enterprise batch prediction

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An AI-assisted plugin for EKS-to-GKE migrations with built-in governance

Cloud Computing /

Enterprises are increasingly standardizing on Google Kubernetes Engine (GKE) to run their most critical and AI-driven workloads. From Cloud Storage FUSE for high-throughput data access to custom compute classes (CCC) and advanced GPU slicing, GKE provides the scale and efficiency required for modern applications. However, migrating complex Kubernetes environments from AWS EKS to GKE has

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Power your agents: Gemini 3.8 Live with Live Avatar is now generally available

Cloud Computing /

Following our announcement of Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking last week, we are thrilled to share that Gemini 3.8 Live with Live Avatar is now generally available in Gemini Enterprise. First previewed at Google Cloud Next 2026, the technology is now officially ready for enterprise production. As enterprise voice AI evolves

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AlloyDB delivers PostgreSQL for agents: Real-time data at agent scale, with full workload isolation

Cloud Computing /

Enterprises rely on mission-critical operational databases where performance slowdowns simply aren’t an option. Yet when even a few agents execute dense reasoning loops, the unpredictable surge in queries can easily overwhelm traditional architectures. Today, we’re announcing that AlloyDB delivers PostgreSQL for agents (in preview), enabling real-time data access without compromising your mission-critical systems. AlloyDB now

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How growing Latin American midsize businesses are building in the AI era

Cloud Computing /

Latin America’s small and medium-sized businesses are the heartbeat of the region’s economy — accounting for more than 60% of total employment in the region, according to United Nations estimates. And just like their enterprise peers, everywhere you look, ambitious teams are moving fast to embrace AI.  Many have already transitioned from experimenting with generative

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A new, no-compromises database architecture for the agentic era

Cloud Computing /

Entire database engineering careers have been spent on a single question: How do you scale an OLTP workload without compromising the system of record that owns the data? Exadata answered the question by offloading queries into a scale-out storage tier beneath the database, removing the network as the bottleneck. Azure SQL Hyperscale did it with

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