Operations | Monitoring | ITSM | DevOps | Cloud

Previously, we showed you how to securely pull Docker images from Cloudsmith to Kubernetes using OIDC with a CronJob-based approach. We concluded the post discussing credential provider plugins from Kubernetes 1.20 and an enhancement in Kubernetes 1.33 that offers a new approach for external registries like Cloudsmith. We have now built a credential provider that takes advantage of this new capability. This article explores what this means for the future of pulling images from Cloudsmith on Kubernetes.

AI assistants will shape the future of the software supply chain, and today, we’re sharing a glimpse of a powerful idea in motion: Cloudsmith MCP, a proof of concept server that connects large language models (LLMs) like ChatGPT and Claude directly to your software supply chain using the emerging Model Context Protocol (MCP) standard.

When a container starts consuming excessive memory or becomes unresponsive, you need a way to shut it down. The two primary options — docker stop and docker kill,both terminate containers, but they operate differently and have different implications. The key difference: docker stop sends SIGTERM for a graceful shutdown, then escalates to SIGKILL if the process doesn’t exit in time. docker kill skips straight to SIGKILL, terminating the container immediately.

To really make an impact, platform engineering teams need to start thinking like product managers. That means deeply understanding their users, measuring outcomes instead of outputs, and tying everything they do to real business value. Organizations who care about total cost of ownership and fast time to value are adopting this mindset.

We are excited to announce that Netdata has officially implemented the Model Context Protocol (MCP), joining the forefront of AI-powered infrastructure monitoring.

The conversations around artificial intelligence (AI) typically revolve around its vast potential: writing applications, automating tasks, or transforming entire industries. However, despite the excitement around AI’s potential, the more pressing issue for many organizations is how to manage the risks of deploying it at scale across the enterprise. This is where AI Security Posture Management (AI-SPM) comes into play.

We ran real-world load tests across seven different infrastructure plans—from Grid to Dedicated Split—using realistic conversion rates, bot traffic blends, and ERP-driven API imports. The findings were clear: performance scales predictably with resources, but only if your code, cache, and configuration keep up. This blog post walks through key results, why API load is disproportionately expensive, and what metrics matter most. How well does Shopware actually perform under load?