Node.js was introduced over 20 years ago and remains a popular choice for web application teams with over 30 million users. Sites such as Netflix, PayPal, and Uber continue to rely on Node.js to build and maintain their applications.

In the dynamic world of container orchestration, Kubernetes stands out as the undisputed champion, empowering organizations to scale and deploy applications seamlessly. Yet, as the deployment scope increases, so do the associated Kubernetes workload costs, and the need for effective resource capacity planning becomes more critical than ever. When dealing with containers and Kubernetes you can find yourself facing multiple challenges that can affect your cluster stability and your business performance.

Welcome to the next chapter of Grafana Loki! After five years of dedicated development, countless hours of refining, and the support of an incredible community, we are thrilled to announce that Grafana Loki 3.0 is now generally available. The journey from 2.0 to 3.0 saw a lot of impressive changes to Loki. Loki is now more performant, and it’s capable of handling larger scales — all while remaining true to its roots of efficiency and simplicity.

We are thrilled to announce the preview of Explore Logs, a new way to browse your logs without writing LogQL. In this post, we’ll cover why we built Explore Logs and we’ll dive deeper into some of its features, including at-a-glance breakdowns by label, detected fields, and our new pattern detection. At the end, we’ll tell you how you can try Explore Logs for yourself today. But let’s start from the beginning — with good old LogQL.

New in Cribl 4.5, the Model Driven Telemetry Source enables you to collect, transform, and route Model Driven Telemetry (MDT) data. In this blog, you’ll learn how to explore the YANG Suite to understand the wide variety of datasets available to transmit as well as how to configure the tools to get data flowing from Cisco IOS XE network devices to Cribl Stream.

Numerous companies keep launching AI/ML features, specifically “ChatGPT for XYZ” type productization. Given the buzz around Large Language Models (LLMs), consumers and executives alike are growing to assume that building AI/ML-based products and features is easy. LLMs can appear to be magical as users experiment with them.