Operations | Monitoring | ITSM | DevOps | Cloud

How AI Turns Monitoring From "What Now?" Into "What's Next?"

It's 3 AM. Your phone starts buzzing with alerts, and you stumble to your laptop only to be greeted by a dashboard that looks like the control panel of a nuclear reactor in meltdown: Red lights everywhere. Numbers that should be green are decidedly not green. And your brain, still foggy from sleep, is asking the most fundamental question in all of IT operations: "Okay, yes, there's clearly a problem... but, now what?".

The Blind Spots That Haunt Legal IT

In a recent survey, Udacity’s team explored the evolving landscape of AI adoption by asking 2000 professionals (including those in the legal sector) if they used AI. Unsurprisingly, over 90% of respondents said they did. More concerning, 72% of managers reported personally paying out of pocket for AI tools to use at work, introducing uncontrolled risk into corporate environments.

How GenAI is Shaping Elastic Customer Support

Discover how GenAI has accelerated Elastic's customer and support efficiency. Built on Elastic’s Search AI Platform, the Support Assistant delivers self-service in-product customer support and capacity gains within our support function. Julie Rudd, VP of Support at Elastic, shares how it speeds up issue resolution by combining generative AI with Elastic’s deep knowledge base. Hear directly from a support engineer how the Support Assistant streamlines case resolution and helps engineers and customers find answers faster.

The Strategic Imperative: Transforming Platform Sunset into Competitive Advantage

With innovation cycles accelerating, product end-of-life announcements have become an inevitable reality. Infoblox NetMRI, for example, has reached end of life with license sales ending April 2025 and support shutting off by early 2027. Whether it’s a network management platform, IT monitoring system, or enterprise application, the sunset of critical business tools forces organizations into what many view as disruptive, costly transitions.

I turned error messages into a sales machine (by accident)

Dan Mindru is a Frontend Developer and Designer who is also the co-host of the Morning Maker Show. Dan is currently developing a number of applications including PageUI, Clobbr, and CronTool. I find it remarkable that we’re getting so many AI startups every day. As software engineers, most of us like to know what our software is actually doing. We plan, review, and perform automatic tests to verify it’s working as expected. Then we do a round of manual testing for good measure. Not with AI.

From Feathers to Fiber: The Fast Lane of Battlefield Intel

Carrier pigeons once flapped through war zones with vital messages. Today? Sensor fusion delivers battlefield intel faster than thought. The fight isn’t just about who has the data, it’s about who moves on it first. Speed wins wars now! Read how it’s changing the game, how deployable command and control (C2) nodes can help, and why network slicing is a key ingredient in building a resilient, high-speed transport network: Carrier Pigeons to Sensor Fusion: Speed Matters in Information.

AWS Prometheus: Production Patterns That Help You Scale

You've got Prometheus running in one cluster — maybe a dev environment, a single EKS cluster, or a proof-of-concept setup. The configuration is straightforward: node_exporter on a few EC2 instances, some service discovery for pods, and a single Prometheus server scraping everything. Storage is local, retention is 15 days, and you can keep all the default recording rules without worrying about costs.

Instrumenting the Node.js event loop with eBPF

Recently, I was testing Coroot’s AI Root Cause Analysis on failure scenarios from the OpenTelemetry demo. One of them, loadgeneratorFloodHomepage, simulates a flood of excessive requests. As expected, it caused a latency degradation across the stack. Coroot’s RCA highlighted how the latency cascaded through all dependent services. At the same time, we noticed a moderate increase in CPU usage for the frontend service and the node itself.

Synthetic Monitoring Frequency: Best Practices & Examples

Synthetic monitoring is, at its core, about visibility. It’s the practice of probing your systems from the outside to see what a user would see. But there’s a hidden parameter that determines whether those probes actually deliver value: frequency. How often you run checks is more than a technical configuration—it’s a strategic choice that ripples through detection speed, operational noise, and even your team’s credibility.