Operations | Monitoring | ITSM | DevOps | Cloud

The best engineering teams ship quality software quickly, but doing that consistently requires more than just speed. It requires careful attention to reliability, security, ease of maintenance, and developer experience. The Engineering Excellence Summits were designed to create a community of engineering leaders looking to connect with others facing similar challenges, share approaches that are working, and learn what “better” can look like.

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?

AI agents are undoubtedly exciting. What company would turn down the opportunity to use an intelligent bot that can make decisions, perform complex tasks, and take on the tedious work employees would normally have to deal with? On paper, AI agents sound like tremendous time and money-savers. The reality, however, is a bit different from the fantasy.

Researchers at Trend Micro have uncovered a critical unauthenticated remote code execution (RCE) vulnerability affecting Langflow versions prior to 1.3.0. Langflow is a Python-based visual framework for building AI applications and boasts over 70,000 stars on GitHub and over 21,000 global weekly downloads from the public PyPI upstream. Source: Cloudsmith Navigator Versions released before 1.3.0 contain a serious flaw in the code validation logic, which allows arbitrary code execution.

We are in mid-2025, and teams across industries are rolling out large language models, or LLMs, to power everything from conversational agents to document understanding. However, getting them to run smoothly in production… That’s still a challenge. A working model isn’t just about putting it in a container and tossing it into a Kubernetes cluster.