Apple’s AirPods with cameras for AI are apparently close to production

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Agent Executor, Google’s distributed Agent Runtime

As models and harnesses improve, agents are taking on increasingly complex tasks that can run for hours or even days. But as we push agents to do more, this has surfaced a new operational problem: long-running agent workflows are fragile and incredibly hard to manage reliably and efficiently in production.

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Bringing you Agent Sandbox on GKE and Agent Substrate

In just a short time, we’ve seen AI transition from simple chat interfaces to autonomous agents capable of function calling, code execution, and persistent terminal use. But to orchestrate these capabilities securely, agents need more than just intelligence — they need a robust, hyper-scalable, secure compute environment in which to

Benchmark LLMs on-device with AI Edge Portal

LLMs have become more powerful at smaller sizes, but deploying them to edge devices like smartphones remains a massive challenge. Today, developers have to optimize across a sprawling combination of accelerators, operating systems, and countless System-on-a-Chip (SoC) configurations, often relying on manual testing with just a handful of devices. Google

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Urban Outfitters moves Sterling OMS to AlloyDB for PostgreSQL

Editor’s note: Urban Outfitters, Inc. (URBN) recently completed a major infrastructure upgrade, migrating its IBM Sterling Order Management System (Sterling OMS) from an Oracle database to Google Cloud's AlloyDB for PostgreSQL. This strategic move, a testament to the growing partnership between Google Cloud and IBM, delivers significant

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Agent Executor, Google’s distributed Agent Runtime

Bringing you Agent Sandbox on GKE and Agent Substrate

Benchmark LLMs on-device with AI Edge Portal

Urban Outfitters moves Sterling OMS to AlloyDB for PostgreSQL