If you are starting LLMO (Large Language Model Optimization) in Japan, I recommend focusing your efforts on two specific AIs: Google AI Overviews and Perplexity. Because AI Overviews and Perplexity ar ...

Discover 20 practical AWS cost optimization strategies for EC2, EKS, S3, RDS, and networking, with pricing examples and a ...

The Conclusion of This Article AIO content is text that AI can extract as evidence to recommend companies or services when ...

Explore why modern data lakehouses slow down at scale and how metadata growth, small files, partitioning, and maintenance ...

Introduction As datasets grow, processing them efficiently on a single machine can become challenging due to limitations in ...

Research reveals how Google AI Overviews retrieve and cite sources, what drives visibility, and which optimization practices work.

BigQuery ObjectRef is now GA. Combine unstructured data with Gemini to build powerful multimodal analytics and conversational agents using SQL.

A comprehensive guide to generative engine optimization, covering strategy, structure, metrics, and how AI discovery really ...

Built on technology from Logline, which Grafana Labs acquired in April 2026, Cloud Logs now offers customers faster full-text search, at no additional cost.

Spatial SQL and spatial joins are now supported in Lakehouse//RT and can run up to 38x faster than traditional data ...

Every token counts. Whether you're building production applications with large language models (LLMs) or running experiments in a notebook, bloated prompts silently drain budgets and degrade response ...

Lakebase is cost-efficient by design because its separated storage and compute architecture lets branching, read replicas, and high availability share one storage layer, while serverless autoscaling ...