Operations | Monitoring | ITSM | DevOps | Cloud

Large language models (LLMs) are machine-learning models specialised in understanding natural language. They became famous once ChatGPT was widely adopted around the world, but they have applications beyond chatbots. LLMs are suitable to generate translations or content summaries. This blog will explain large language models (LLMs), including their benefits, challenges, famous projects and what the future holds.

Kubernetes 1.28 is currently planned to be released on the 15th of August 2023. Canonical Kubernetes 1.28 is scheduled to release swiftly afterwards, with MicroK8s 1.28 expected to be generally available within 24 hours of upstream, and Charmed Kubernetes expected to follow within days. We consistently follow the upstream release cadence to provide our users and customers with the latest improvements and fixes, together with security maintenance and enterprise support for Kubernetes on Ubuntu.

Unplanned downtime is an eventuality every business tries to avoid but will face. In today’s digitally interconnected world, outages can be particularly damaging, especially if the business is unprepared. Not only can outages cause employee frustration and anger customers, leading to numerous intangible costs like lower satisfaction hurting a company’s reputation, but the loss of employee productivity caused by unplanned downtime can significantly affect the bottom line.

Many thanks to everyone who joined us for our recent virtual meetup, during which we discussed some of our Q2 2023 highlights, including features highlights, the 2023 roadmap for VictoriaMetrics and of course: The launch of VictoriaLogs! In this blog post, we’d like to share a summary of these highlights.

We are happy to announce the release of Icinga 2.14.0 and 2.13.8 today. Especially the 2.14.0 release comes with a lot of fixes and improvements and this blog post will highlight the most important ones. There are some breaking changes in 2.14.0, so please make sure to read the corresponding section before upgrading.

If you have ever worked in a kitchen, you know how tough it is to be short-staffed. Cooks have to work twice as hard and their performance suffers, leaving not-so-happy customers and comped meals for the complainers. It’s similar to how applications operate. Maybe an application is glitching out from poor coding on the backend or bogging from an influx of data coming in. In either case, the end-user application experience suffers.

In the dynamic landscape of software and web applications, failures can have severe consequences, impacting user experience, business continuity, and overall performance. To proactively address these challenges, organizations rely on robust monitoring practices supported by failure metrics. Failure metrics, specifically tailored to software and web application monitoring, provide crucial insights into system health, reliability, and optimization opportunities.

Greetings, Kubernetes Time Lords! Through a series of recent updates to our multi-purpose Kubernetes Monitoring solution in Grafana Cloud, we’ve made it easier than ever to assess your resource utilization, whether you’re looking at yesterday, today, or tomorrow. All companies that use Kubernetes, regardless of size, should monitor their available resource utilization. If a fleet is under-provisioned, the performance and availability of applications and services are at serious risk.

Web performance is a critical aspect of delivering exceptional user experiences. To measure and optimize website performance, various tools are available, each offering unique features and capabilities. In this article, we will compare two popular web performance tools: Lighthouse and Sitespeed combined with Graphite. We'll explore the strengths and benefits of each toolset, helping you make an informed decision for your performance testing and optimization needs.

When your business is all about providing cloud services at the edge, optimizing the quality of your network connectivity is paramount to customer success. Learn how StackPath saved $3 million and optimized their network performance by using Kentik.