Operations | Monitoring | ITSM | DevOps | Cloud

Distributed traces track how your system processed a single request — not what your customers did over time. Confusing the two leads to poorly instrumented systems.

How we built an AI agent that implements Jira tickets, creates merge requests, and tracks them on its own, and our journey to get to that point.

We’re excited to announce that Speedscale’s proxymock is now available as an OpenClaw skill on ClawHub, bringing powerful API traffic replay capabilities directly into your Claude workflows.

Kubernetes operators extend the Kubernetes API with custom logic, automating tasks like provisioning, configuration, and policy enforcement. Instead of managing these tasks manually or with ad hoc scripts, Operators codify your workflows into controllers that run natively inside the cluster. In this tutorial, you’ll build a simple operator using Go and Kubebuilder; a framework that scaffolds much of the boilerplate so you can focus on core logic.

The explosion of AI has created a continuous demand for computing power. At the heart of this need sits one critical resource: GPUs. They have become the hardware of choice for AI and machine learning, particularly deep learning workloads that operate on enormous data sets. However, as organisations race to train larger models and deliver faster inference, many are discovering that owning and operating GPU infrastructure isn’t always practical.

Previously I have written about how useful public cloud storage can be when starting a new project without knowing how much data you will need to store. However, as datasets grow over time, the costs of public cloud storage can become overwhelming. This is where an on premise, or co-located, self-hosted storage system becomes advantageous: it provides the greatest range of benefits, including cost, performance, security, and data sovereignty.