Operations | Monitoring | ITSM | DevOps | Cloud

In the high-stakes world of modern infrastructure management, "move fast but break things" is not a viable strategy. As organizations scale their digital footprints, the competing demands of velocity and vulnerability have created a new operational reality. Today, the integrity of your infrastructure is synonymous with the integrity of your business. For system administrators and DevOps engineers, the landscape has shifted. It is not enough to simply provision servers and deploy applications.

Being on-call can sometimes feel overwhelming. If that feeling goes unnoticed for too long, it often translates into burnout. And early burnout signs usually show up in ways, like how people respond to incidents or how they feel about the schedule. This guide walks through four such signs that can be useful to watch for before on-call burnout sets in.

Open source libraries are repositories of code that developers can use and, depending on the license, contribute to, modify, and redistribute. Open source libraries are usually developed on a platform like GitHub, and distributed using package registries like PyPI for Python and npm for JavaScript. These repositories contain pre-written, re-usable code that developers use to add elements or features within their software projects.

We’re excited to announce support of physical environments in the Terraform Provider for Kosli! What’s Included Environment Management: Full lifecycle support for creating, updating, and managing physical environments types: K8S, ECS, S3, docker, server, and lambda. Manage legacy environments as IaC: Import your existing physical environments to have Terraform manage them.

AI-native SaaS products aren’t failing because the models are bad. They’re failing because the architecture can’t keep up with how AI actually behaves in production. What looks affordable in staging can erode your margins once real customers, workflows, and automation come into play. Designing AI-native SaaS architecture is now as much a margin decision as it is a technical one.

Every SysAdmin knows the feeling. They are swimming in logs—terabytes of them. Every daemon, service, and kernel subsystem religiously writing their activities to syslog. The data exists. The signals are there. Yet, somehow, incidents still are still unpredictable. How is this even possible? Here's why this happens: Traditional syslog infrastructure was designed for storage and retrieval, not detection and response.

You’ve probably heard of OpenClaw. Maybe you’ve seen the demos where an AI agent opens a browser, navigates to your CRM, fills in a form, and files a support ticket. No API required. Maybe you thought “that’s cool but I’d never run that at work.” Your employees already are. According to Permiso’s research, 22% of enterprise customers have employees running OpenClaw without IT approval.

Traditional synthetic data generation approaches, still called “Test Data Management” (TDM) by legacy vendor, were designed for a world where applications were monolithic, databases were the center of gravity and change happened slowly. The world looks a lot different now. Modern systems are distributed, often times event-driven, and increasingly powered by streaming data and AI agents. In this environment, batch-oriented synthetic data generation fails to capture how systems actually behave.

In Part 1 and Part 2 we explored why testing modern software is so difficult. Production data is the most valuable input for testing, but it’s locked away because it contains PII and sensitive context. Traditional Synthetic Data Generation (SDG) was built for batch databases, not streaming systems. And AI coding agents amplify every weakness in existing test strategies because they need current, realistic data or they generate buggy code based on outdated assumptions.

Most teams choose standard on-call patterns like weekly or daily rotations. But sometimes a less conventional rotation can solve a specific problem or just fit better with how your team works. This guide walks you through five offbeat on-call rotations. For each, we look at why it might work for you and the challenges involved. This helps you see the full picture before you decide to try them out. Let’s dive in!