93 articles
Google Cloud's monthly AI recap covers major model launches, security acquisitions, and developer resources for March, February, and January 2026.
Vail Resorts built My Epic Assistant, a multi-agent AI system using Google Gemini to deliver personalized ski resort recommendations at scale.
This post explains how to build production-ready AI agents using Google-managed Model Context Protocol (MCP) servers for secure, scalable enterprise deployments.
UC Berkeley student research reveals how the next generation of developers is building genuine AI literacy rather than dependency.
FM Logistic used Google Cloud's AlphaEvolve to optimize warehouse routing by evolving a better traveling salesman algorithm, achieving a 10.4% efficiency gain over an already-optimized baseline.
This post introduces Dynamic Resource Allocation (DRA), a new Kubernetes-native framework for managing hardware accelerators like GPUs and TPUs at scale.
Google Cloud announces llm-d as an official CNCF Sandbox project, positioning Kubernetes as the foundation for large-scale AI inference infrastructure.
This post provides a technical guide for developers on optimizing AI model training using Google's seventh-generation Ironwood TPU within the JAX and MaxText ecosystems.
Google announces the preview of multi-cluster GKE Inference Gateway for scalable AI/ML inference workloads across multiple GKE clusters and regions.
Google Cloud and NVIDIA announce expanded AI infrastructure partnership at GTC 2026, introducing new hardware, software, and platform innovations.
This article covers architectural best practices for building resilient LLM applications on Vertex AI to minimize 429 (Resource Exhausted) errors.
This guide covers best practices and prompting frameworks for Nano Banana image generation models built on the Gemini 3 family.
BMW Group and Google Cloud built an automated workflow for fine-tuning, optimizing, and evaluating small language models (SLMs) for in-vehicle deployment.
