Operations | Monitoring | ITSM | DevOps | Cloud

Template variables are one of the most used features in Grafana, providing users with a dropdown to turn one dashboard into hundreds: pick a different cluster, region, or service, and every panel updates. For most of Grafana's history, variables had a single home at the top of the dashboard. Each dashboard had one flat list of variables, and that list was shared by every panel on it. But as dashboards grew in size and number, those controls became harder to organize and maintain.

Monitoring used to mean checking that your dashboards looked fine and starting to troubleshoot when something broke. That approach is outdated. Today, the health of your applications directly affects your business. A slow checkout page, a lagging database, or an unresponsive server costs you customers, revenue, and trust. A problem that once took hours to identify and fix is no longer acceptable. This is where AIOps comes in.

I used to joke that I got my best parenting ideas from management books, and my best management ideas from parenting books. Now, I'm extending it to agents. Hear me out. When you're teaching a teen how to drive, you don't hand them the car keys on day one. You start in an empty parking lot. You ride along and ask them to talk through what they're doing.

Nokogiri sits under so much Ruby code that its patch releases are worth reading even when the version number looks routine. 1.19.5 is a patch release carrying three security advisories and a long list of crash and memory fixes, so it’s worth pulling in on your next dependency pass.

Does your IT team know which applications, users, or devices are consuming the most network bandwidth? A video call freezes, file transfers slow down, or a network link becomes congested. Without traffic analysis, finding the source of the problem can take hours. Network analyzer software helps your IT team: However, tools differ in how they collect traffic data. Some analyze flow records, while others also inspect packets to provide more detail.

Why does an application that runs well in the data center slow down once one of its tiers moves to AWS or Azure? Often the servers are healthy and the cloud console shows no faults; the delay lives in the connection between them, where neither view reaches. That blind spot shows up in familiar ways: So these are design problems before they are operational ones.

Improving an AI agent often involves changing its prompts, models, retrieval methods, or tools. But determining whether those changes actually improve performance can be difficult. A revised prompt might produce better answers for a handful of test cases but perform poorly on unexpected user requests. A cheaper model might reduce token costs while introducing tool-calling errors or longer execution times.

Sometimes you just want to fetch all Icinga 2 objects of a given type from the API and do something with them. Then you notice that Icinga 2’s memory usage jumps up with every large request and never comes back down, as if each HTTP request leaked a huge chunk of memory. And once enough of these requests run in parallel, no matter how much RAM the machine has, Icinga 2 runs out of memory and crashes. Here’s what was really going on, and how we fixed it in Icinga 2 v2.16.