Today, we are releasing Gemma 4 on Google Cloud. What’s new: It is, byte for byte, the most capable family of open models. Built from the same research as Gemini 3 and released under a commercially permissive Apache 2.0 license, these models move beyond chat. With context windows up to 256K, native vision and audio […]
Introducing Gemma 4 on Google Cloud: Our most capable open models yet Read More »
Ryan sits down with Galen Wolfe-Pauly, CEO of Tlon, to chat about calm computing and how humans can take back ownership of their data and digital world.
Seizing the means of messenger production Read More »
Building the perfect bra takes thousands of data points. That’s why Honeylove isn’t just another intimates brand. We’re a technology company that happens to make exceptional bras, tops, shapewear, and bodysuits. Technology shapes everything we do, from how we iterate garments based on customer feedback to how we optimize sizing across those thousands of data
How Honeylove boosts product quality and service efficiency with BigQuery Read More »
Smithy Kotlin client code generation is now generally available. With Smithy Kotlin, you can keep client libraries in sync with evolving service APIs. By using client code generation, you can reduce repetitive work and instead, automatically create type-safe Kotlin clients from your service models. In this post, you will learn what Smithy Kotlin client generation is,
Smithy Kotlin client code generation now generally available Read More »
Written by: Stuart Carrera Introduction Building on recent BRICKSTORM research from Google Threat Intelligence Group (GTIG), this post explores the evolving threats facing virtualized environments. These operations directly target the VMware vSphere ecosystem, specifically the vCenter Server Appliance (VCSA) and ESXi hypervisors. To help organizations stay ahead of these risks, we will focus on the
vSphere and BRICKSTORM Malware: A Defender’s Guide Read More »
When discussing applications and systems using generative AI and the new opportunities they present, one component of the ecosystem is irreplaceable – data. Specifically, the data that companies gather, hold, and use daily. This data serves as the backbone for applications, analytics, knowledge bases, and much more. We use databases to store and work with
Activating Your Data Layer for Production-Ready AI Read More »
Adoption and trust are moving in diametrically opposed directions, and that gap has real implications for organizations deciding how to spend money on software.
What the AI trust gap means for enterprise SaaS Read More »
Workplace safety has improved dramatically over the past several decades. According to the Bureau of Labor Statistics, occupational injury rates in the United States have declined by more than 60% since the early 1970s. This is driven by stronger regulations, better training programs, and a growing culture of safety-first operations. Despite this progress, the International
Automate safety monitoring with computer vision and generative AI Read More »
With Cloud Next ’26 rapidly approaching, we couldn’t be more excited to showcase how Google Cloud empowers organizations to scale smarter, faster, and more efficiently. Be sure to anchor your experience by attending the Infrastructure spotlight by Mark Lohmeyer, VP & GM of AI and Computing Infrastructure, where he will detail the future of our
Top Infrastructure and GKE Sessions at Cloud Next ’26 Read More »
As AI workloads transition from experimental prototypes to production-grade services, the infrastructure supporting them faces a growing utilization gap. Enterprises today typically face a binary choice: build for high-concurrency, low-latency real-time requests, or optimize for high-throughput, “async” processing. In Kubernetes environments, these requirements are traditionally handled by separate, siloed GPU and TPU accelerator clusters. Real-time
Run real-time and async inference on the same infrastructure with GKE Inference Gateway Read More »
