Operations | Monitoring | ITSM | DevOps | Cloud

Automated Diagnostics & Remediation reduces MTTR by closing the gap between detection, diagnosis, and resolution.

LogicMonitor’s latest innovations span the entire platform to deliver the operational foundation enterprises need for Autonomous IT—complete visibility from infrastructure to end user, AI that reasons in full context, and closed-loop automation that moves from detection to resolution. Over 90% of organizations rely on at least two to three monitoring solutions—and many enterprises operate five or more.

Most legacy AIOps investments underdeliver because the AI lacks context, not capability. LogicMonitor’s latest innovations expand Edwin AI’s contextual intelligence across every dimension, so recommendations are accurate, explainable, and trusted by the teams that need to act on them. Reduce incident resolution time with AI that understands your environment—not just your alerts.

In January 2024, I wrote about removing all advertising cookies and user tracking from sentry.io. It was eight months into the decision at the time, and we were still figuring out what broke and what surprised us. That post struck a nerve: it became one of the most-read things we’ve ever published, probably because everyone building or running a product on the web was watching the same cookie deprecation timeline and wondering what would actually happen if someone just ripped the bandaid off.

N+1 queries are the most common performance problem in Rails applications. ActiveRecord’s lazy loading means every belongs_to, has_many, and has_one association is a potential N+1 waiting to happen. The good news is that Rails gives you multiple ways to fix them, and tools like Scout can find them automatically. This guide covers everything a Rails developer needs to know about N+1 queries: what they are, how to fix them, how to prevent them in CI, and how to detect them in production.

If you’re running infrastructure on Azure, you’ve probably dealt with the split between your Azure-native monitoring and the rest of your stack. Your VMs, databases, and Kubernetes clusters generate platform metrics through Azure Monitor, but those metrics live in a separate world from the OS-level, application, and on-prem metrics you’re already watching in Netdata.

Observability in software is often framed as a choice between self-hosted and SaaS: manage it yourself, or pay a vendor to handle your data. Both self-hosted and SaaS approaches have their merits, but assuming you must choose one exclusively over the other leads to poor trade-offs: either overcommitting to an all-in-one SaaS despite spiraling costs, or fully self-hosting when it’s unnecessary.

Coroot's node-agent already collects CPU profiles for any process on the node using eBPF, with zero integration from the application side. For Java, we dynamically inject async-profiler into the JVM to get memory and lock profiles. But Go processes were still a blind spot for non-CPU profiling unless the app exposed a pprof endpoint and the cluster-agent scraped it. We wanted the same zero-config experience for Go heap profiles. This post is about how we got there.