Operations | Monitoring | ITSM | DevOps | Cloud

The tools we use shape how we see problems. When flow and metrics are siloed, so is your visibility.

For many businesses, 2020 marked the dawn of the cloud-first era, with organisations around the world embracing public cloud. And it made sense at the time; promise of reduced infrastructure costs, flexibility and scalability meant that leveraging cloud services was a no-brainer. But with any new technology, the shifting tides that come along with its proliferation also informs the cyclical nature of its adoption.

Imagine incidents resolved through insights, not manual investigations. ‍ Picture an incident management future where you're never alone during critical alerts. Imagine your best engineer always available, tirelessly investigating issues, analyzing logs, correlating metrics, checking recent code changes, and delivering actionable insights, instantly. Today, ilert is stepping boldly into this future with our first intelligent agent: ilert Responder.

If you've ever had to debug a Flutter app after an error report that just says “Null check operator used on a null value,” you already know: context is everything. And context can be hard to come by when you’re juggling native code, Dart, async stack traces, and platform channels. With v9 of our Flutter SDK, we’re introducing some features to help you get even more visibility into what’s going wrong, with the insights to make it better. Here’s what’s new.

In a perfect world, log anomalies would speak clearly and never at 2 a.m. But in reality, log data is massive, alerts can be cryptic, and critical issues often get buried in the noise. That’s why choosing the right log management tool is crucial, it’s the first line of defense against downtime, breaches, and costly oversights. This blog breaks down some of the top log management tools on the market, what they do well, where they stand out, and how they fit into your stack.

Kubernetes makes it easy to scale applications. But when it comes to CPU resource management, a poorly tuned cluster can quickly become unstable or inefficient. For network engineers, setting CPU requests and limits correctly—and understanding the deeper implications—is essential for keeping workloads efficient, costs predictable, and noisy neighbors in check.

In Grafana Cloud, we offer a range of AI capabilities to support your observability needs, including a feature for forecasting on any of your metrics and coupling it with Grafana Alerting. This is critical functionality if you want to make the switch from reactive to proactive alerting, as troubleshooting a problem before it arises is an important part of modern observability.

AI discourse these days is all over the place. Depending on who you talk to, AI’s are absolute flash-in-the-pan junk, or they’re the best thing since sliced bread. I want to cut through the noise, though, and see for myself what someone can do out here on the bleeding edge. Thus, I’m setting myself a challenge: write a usable—and useful—application with Claude Code, from soup to nuts. Here are the rules: With our ground rules established, let’s figure out our app!

AI adoption is accelerating across nearly every industry. According to McKinsey’s 2025 State of AI report, 78% of organizations now use AI in at least one business function, up from just 55% the year prior. From real-time analytics to generative tools and process automation, AI is becoming a fundamental part of how modern businesses operate and compete.

Insecure system configuration is a textbook example of how neglected settings can create an entry point for attackers targeting your CI/CD pipelines. It’s rarely the cutting-edge zero-day that causes a breach. More often, it’s the unpatched service, the overly permissive role, or the default password that was never changed. While this risk overlaps with CI/CD credential hygiene (covered in Part 6 of our OWASP CI/CD series), the focus here is much broader.