Operations | Monitoring | ITSM | DevOps | Cloud

For a long time, robotic process automation (RPA) was the fastest way to scale repetitive digital work. Bots copied, clicked, and executed rule-based tasks faster than any human. They reduced error rates and delivered early wins for efficiency. Sounds just fine, right? Prepare for a Matrix moment, because the truth is that IT teams built RPA only for predictability. It could follow instructions, but it couldn’t adapt when something unexpected happened.

Dashboards and alerts can tell you something’s wrong, but they don’t immediately tell you why. A red indicator or synthetic test failure prompts detective work. You flip between dashboards, timestamps, and logs, trying to line up what the check saw with what the system did. Now imagine your monitoring could explain itself by sending traces directly into your OpenTelemetry (OTel) backend.

The cloud has long promised limitless scalability and near-perfect uptime. But if you tried to access your Microsoft 365 dashboard or recline your smart bed last week, and got nothing but a spinning icon, you weren’t alone. In the span of 10 days, both Amazon Web Services (AWS) and Microsoft’s Azure Cloud suffered widespread outages that rippled across industries.

After three days of demos, sessions, and hallway conversations at KubeCon Atlanta, one thing became clear to CloudZero CTO Erik Peterson: the cloud-native world is shifting from cost control to value engineering. Teams aren’t just fighting bills anymore. They’re fighting complexity, GPU scarcity, Kubernetes sprawl, and pressure from the business to justify every dollar of technical investment. And this year’s KubeCon attendees? They were ready for those conversations.

Another day, another massive Internet disruption, and this time it’s Cloudflare taking huge parts of the Internet offline. This incident is not an anomaly. It is part of a recurring pattern that has become standard in digital infrastructure. We have reached an inflection point in digital operations. Outages at major cloud and content delivery network (CDN) providers are now expected. The only real uncertainty is when it will happen next.

Production errors can feel overwhelming. Your Rollbar dashboard is filling up with alerts, your team is scrambling to understand what needs immediate attention, and critical revenue-impacting issues might be buried among less urgent problems. Sound familiar? In this post, I'll walk you through a workflow that transforms production error chaos into organized, prioritized action items. We'll cover everything from analyzing Rollbar errors to creating properly linked Linear tickets.

‍ Like many developers, I’ve spent the last year adjusting to a world where I’m spending more keystrokes arguing, or, “explaining” to an agent the code I’d like to be written. No longer must we toil, writing every line code by hand like our great grandparents probably did.

Bitbucket Cloud is more than a code hosting platform. We’re an enterprise partner, helping teams code together at scale with security, compliance, and flexibility at every step. As part of the Atlassian Cloud platform serving more than 300,000 organizations around the world, we’re continuing to build the next generation of Bitbucket Cloud as your trusted cloud vendor, whether you’re a global bank, healthcare provider, or a fast-scaling tech company.

Most teams picture incident response as a linear sprint from alert to resolution. A notification appears, an analyst pivots across screens, a decision gets made, and the workflow moves on. It works, but it is mechanical, tiring, and fragile. Graylog 7.0 aims for something more impactful. Guided remediation gives analysts clarity during the moments when pressure rises and context usually scatters. It takes raw detection data and turns it into a clear path forward. No theatrics.

Over the past three decades, Ruby has assumed a pivotal role in the modern web stack and become a fixture in the tool kits of countless DevOps and platform teams. Today, it is a driving force in contemporary application development, testing, automation, and CI/CD. For this blog post, we used data from our always-on continuous profiling of more than 3,000 real-world services from hundreds of organizations to track trends in Ruby usage and performance.