Operations | Monitoring | ITSM | DevOps | Cloud

Summary: Machine learning-based anomaly detection improves observability by learning normal system behavior instead of relying only on static thresholds. This article explains how vmanomaly, its MCP server, purpose-built skills, and an LLM-powered UI copilot help engineers explore telemetry, investigate anomalies, build MetricsQL queries, select suitable models, apply business constraints, and validate configurations through natural language.

If your API answers on QUERY, you can now point a monitor straight at it. QUERY joins HEAD, GET, POST, PUT, PATCH, DELETE, and OPTIONS in the HTTP method list for HTTP, keyword, and API monitors. You can find it under the “Advanced settings” when editing or adding your monitor. QUERY became a standard in June 2026 as RFC 10008. It’s safe, idempotent, and it carries a request body.

For more than a decade, the prevailing wisdom in enterprise IT was simple: move everything to the public cloud. Hyperscale platforms promised unlimited scalability, lower costs, agility and freedom from the burdens of managing infrastructure. Cloud-first has been rapidly gaining momentum as the de facto path to a modern digital footprint. Until now.

Teams running containerized workloads on Google Cloud have long relied on JFrog as their single source of truth for container images. The missing piece has been getting Google Cloud’s own runtime services — like Cloud Run and Google Kubernetes Engine (GKE) — to pull directly from JFrog for every container image pull. I’m happy to say that the gap is now closed. Artifact Registry in Google Cloud has introduced a new repository mode called Connector that addresses this requirement.

You shipped a skill. It worked. You closed the tab. That’s the whole problem. Model choice is a decision you make once, at the moment you’re least equipped to make it: before the skill is even authored. Then you never revisit it, because the skill stopped being interesting the day you got it working. So go back and check. Here’s how.

A strong AI business case ties a specific goal to a measured outcome and a fully-loaded cost. Most fail because they skip one of the three: no clear mandate, an over-broad "AI fixes everything" scope, or a cost estimate that ignores adaptation and error-correction. Build it in six steps: define goals, identify uses, break work into tasks, evaluate models, assess total cost, then launch and refine. Most companies are now spending on AI. Far fewer can show what they got back.

If your engineering teams run on ServiceNow, incidents are where they get work done. Putting cost work into an incident gives it the same path to resolution as any other work item your team handles. When a cost anomaly arrives as an incident, your teams route it, assign it, and resolve it on their usual SLAs. When a savings recommendation arrives as an incident, an engineer owns it and acts on it. Now you can send either straight into ServiceNow.

Icinga Director’s CLI commands have supported import sources and sync rules for a long time, which includes listing, checking and running them. Before Icinga Director v1.11.6 two things you couldn’t do from the CLI, though, were narrowing a health check down to a single object, or deleting an import source or sync rule without opening the web UI. I’ll walk through both, using examples.

For food and FMCG companies, managing freezers becomes more complex once equipment moves outside warehouses and company facilities. Freezers may be placed at supermarkets, retail stores, restaurants, distributors, convenience stores, and other customer locations, often across hundreds of cities or territories. At that scale, spreadsheets and manual field checks make it difficult to answer basic operational questions. Where is a particular freezer? Which outlet is responsible for it?