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In this post, we walk through a practical implementation using KDB-X MCP server integration with Amazon Quick, demonstrating how traders and analysts can ask questions using conversational language and receive actionable insights from datasets. You can apply this same integration pattern across various domains, from financial market analysis to IoT sensor monitoring to DevOps performance dashboards, where you need to simplify access to time series insights.
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Learn how Googlers used AI to produce Google I/O 2026.
Processing millions of user reviews in real-time, under strict latency and cost constraints, is no easy task. Trustpilot has been doing exactly that with custom machine learning since long before large language models (LLMs) were cool. Now, as the company transitions its core stack to generative AI, here is a look at how we teamed up to build a high-volume streaming pipeline using fine-tuned Gemma models. Powering deep review intelligence at scale Trustpilot’s core business relies on delivering
AI agents possess incredible reasoning capabilities and can perform increasingly complex actions. But the reliability of agentic outcomes depends entirely on the quality of the context they can access — context that is frequently locked away in operational databases. To bridge this gap, we are excited to announce the Remote Model Context Protocol (MCP) Server for AlloyDB is now generally available. The Model Context Protocol (MCP) is an open-source standard that gives LLMs a secure, consistent
The example of a growing restaurant Imagine you are running a restaurant chain. You just can't physically feel and touch things to know how your business operates. You need tools and a digital replica of your business to sense the health of the business for you. The friction of growth Growth creates a unique kind of friction that spreadsheets simply weren't built to solve: The bullwhip effect: Small downstream demand shifts swell into upstream inventory tidal waves. SOP drift: Tiny departures fr
Application owners and platform engineers have long faced a difficult choice: spend excessively by over-provisioning to guarantee quick startups, or minimize costs but endure slow cold starts. We are excited to announce a solution to this compromise: Google Kubernetes Engine standby buffers. This builds on the launch of GKE active buffers earlier this year, a native version of the Kubernetes CapacityBuffers API that makes it easy to provision readily available capacity to handle traffic spikes,
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