159 articles
The countdown is officially on. In just a few weeks, the cloud-native ecosystem meets in Yokohama for KubeCon + CloudNativeCon Japan 2026. Taking place on Tuesday, July 28 from 09:00 – 12:30, KeycloakCon Japan brings together…
Global datacentre electricity consumption reaches 1.9% of all generation, with ‘zombie workloads’ wasting 3GW of US capacity alone and a 100MW project delay costing up to $1bn
Report demands answers on Capita’s suitability to run HR and payroll services to 250,000 civil servants
Google has long prioritized practical enterprise needs, focusing on infrastructure, security, and data platforms essential for large organizations. Emphasizing reliability and predictability, Google aims to deliver models that create business impact. Their leadership status in the IDC MarketScape for foundation model software underscores their ability to translate research into secure systems. With the Gemini Enterprise platform, Google provides tools that enable organizations to use AI more effectively, allowing dynamic agents to handle complex workflows securely. The Gemini 3.5 series, designed for intricate tasks, exemplifies this approach, enabling developers and business users to integrate AI seamlessly into their operations.
Running frontier AI in production can be challenging, but Claude on Google Cloud addresses these complexities. It combines Claude’s reasoning capabilities with Google Cloud’s managed infrastructure for ease of use across various enterprise needs. This setup allows for low latency, compliance, and optimized performance, freeing teams from infrastructure management. The platform supports different endpoint types for global reach and regional data needs, ensuring security and cost efficiency, particularly in regulated industries. With features like prompt caching and batch prediction, organizations can enhance performance while managing costs effectively. The integration facilitates a unified approach to both inference and agent functionalities, promoting streamlined development and deployment.
AI is affecting the day-to-day careers of cyber security pros in regard to stress levels, but respondents to an ISC2 data-gathering exercise are split over whether or not their stress levels are going up or down
Google, along with industry partners, has introduced the Agentic Resource Discovery (ARD) Specification, an open standard designed for the publishing, discovery, and verification of AI tools, APIs, and agents. This standard incorporates a discovery layer that utilizes catalogs and registries to facilitate dynamic capability discovery. It also makes use of established protocols such as MCP and OpenAPI, focusing on enhancing trust and interoperability within the AI ecosystem.
When a failed DNSSEC key rollover took down the .al TLD, we deployed a Negative Trust Anchor to restore resolution. This time, though, clients didn’t have to take our word for it: 1.1.1.1 returned EDE 33, a new DNS error code that signals directly in the response that DNSSEC validation was bypassed.
When we first started building kagent, we didn’t run every agent in its own Kubernetes Pod, Service, and ServiceAccount. Instead, agents were simply executed inside the kagent runtime. It was the simplest architecture possible: one runtime…
Can our new Prime Minister bring a new approach to AI that treats the voracious Big Tech companies with less deference?
Google released the Genkit Agents API in preview for TypeScript and Go. The open-source framework packages message history, tool loops, streaming, and state persistence behind a single chat() interface. Detached turns let agents work after clients disconnect. Interruptible tools provide human-in-the-loop control with anti-forgery validation on resume. By Steef-Jan Wiggers
Drawing from the enduring adaptability of HTML and HTTP, Seph Gentle proposes embedding self-contained schemas directly into file headers, ensuring data remains readable without external definitions. His experimental format prioritises forward, backwards, and sideways compatibility, enabling data format evolution without central coordination or data loss By Olimpiu Pop