Operations | Monitoring | ITSM | DevOps | Cloud

If you are treating Large Language Models (LLMs) like simple question-and-answer machines, you are leaving their most transformative potential on the table. The industry has officially shifted from zero-shot prompting to structured AI agent design patterns and agentic workflows where AI iteratively reasons, uses external tools, and collaborates to solve complex engineering problems.

Native crashes on Android have always been harder to debug than they should be. The platform has its own crash reporter (debuggerd) that captures the crashing thread, every other running thread, register state, and memory maps into a file called a tombstone. Tombstones have been a part of Android for a long time; in fact, they’ve been there in one form or another since Android's first commit.

AI SREs are autonomous systems that handle incident triage, root cause analysis, and remediation by correlating logs, metrics, traces, and code signals. However, as they rely on pre-configured telemetry, the critical execution details of a specific failure, such as variable state and code paths, can often be missed. As a result, they either force users into manual redeploy loops or make inferences from partial data, diagnosing issues using probability rather than proof.

A website that loads slowly or an application that freezes mid-transaction tells users something about an organization, whether intended or not. Digital experience monitoring exists to catch these moments before they accumulate into lost customers and frustrated employees. We’ll show you how DEM works, the leading platforms available, and how to select the right solution for specific organizational needs.

Zabbix collects a huge amount of operational data—metrics, alerts, host status, and performance trends. But turning that data into dashboards people actually use is a different challenge. Most teams start with the built-in dashboards. Then the requests start coming: At that point, basic dashboards aren’t enough. Teams start looking for ways to augment Zabbix visualization with tools that improve usability, sharing, and flexibility.

CloudZero’s Umesh Rao and Larry Advey showed what it actually looks like to connect AI to real cloud cost data, and the results are hard to unsee. On April 9, 2026, CloudZero hosted a live webinar, Cost Intelligence for the AI Era, featuring Umesh Rao, Director of Enablement, and Larry “Fred FinOps” Advey, Director of Cloud Platform & FinOps.

In early March, we launched the CloudZero AI Hub and the CloudZero Claude Code plugin, giving customers a direct line to their cloud and AI cost data through natural language. Early adopters and power users have already jumped in, using the plugin to investigate cost spikes, close commitment gaps, and get to cost-per unit metrics that used to take days to pull together. What we’ve noticed over the past few weeks is pretty consistent (and predictable).

If you run a managed service provider, your RMM software is the backbone of daily operations. Remote management, patch cycles, ticketing workflows – it handles the essentials. But if you’re monitoring more than a few dozen client environments, you’ve likely noticed that monitoring and management are not the same thing. And that difference matters more the larger you grow. This post is not about replacing your RMM.

The best server monitoring tools depend on what you actually need to watch. If you want unified metrics, logs, and traces in one SaaS, Datadog wins. For AI-driven root-cause analysis at enterprise scale, Dynatrace is the pick. If you want monitoring, status pages, and on-call scheduling at a flat monthly rate without per-host or per-seat surprises, Hyperping is the best value. For Windows-heavy networks, PRTG. For hybrid IT with deep plugin coverage, Checkmk. For open-source flexibility, Zabbix.