Operations | Monitoring | ITSM | DevOps | Cloud

Last updated: May 2026 Elixir applications have performance characteristics that are genuinely different from Ruby or Python. The BEAM virtual machine handles concurrency through lightweight processes, supervision trees restart failed processes automatically, and Phoenix channels can hold tens of thousands of persistent connections on a single node. These are strengths, but they also mean that the performance problems you encounter are different from what most APM tools were built to detect.

Effective Kubernetes monitoring in 2026 is critical due to increased cluster scale and microservices complexity, demanding a shift toward unified observability (logs, metrics, and traces). The core focus is leveraging AI-driven features to automate anomaly detection, correlate diverse data, and significantly reduce Mean Time to Recovery (MTTR).

Alert fatigue solutions have never been better, but on-call engineers are still burning out. Threshold tuning, AI triage, and alert correlation reduce the noise, but every alert that clears filtering lands with the same incomplete telemetry and triggers the same manual investigation cycle. This post explains why the evidence gap survives every fix, and how runtime context changes that.

The way we build software has drastically changed over the past few years. What hasn’t changed is that this software ends up in front of real people: you, me, my mom. And when those users inevitably run into something broken, you as the application’s developer need to be equipped with the right tools, context and understanding of what broke, where it broke, and how to fix it as quickly as possible. Every day we’re inching closer to self-healing software.

If you integrate DX NetOps with external dashboards, reporting engines, or IT service management tools, you likely rely on our API framework. We are currently migrating this framework from OData 2 to OData 4. This transition requires you to update your existing integrations so they continue to function properly. Let me walk you through exactly what is changing, how to identify your active API queries, and the specific adjustments you need to make to your setup.

Oh Dear keeps a watchful eye on your websites: uptime, performance, SSL certificates, broken links, DNS, cron jobs. If something can quietly break, we're already checking it for you. Today we're connecting that data to a new place: your AI assistant. We just shipped an MCP integration. If you use Claude, Cursor, or any other client that speaks the Model Context Protocol, you can now ask questions like "any broken links on my site?" or "when does my certificate expire?" in plain language.

Your dataset has hundreds of attributes. Some are self-explanatory: http.response.status_code, server.address. Others are not: meta.refinery.reason, dataset.slug, sli.latency_target_ms. If you don't know what an attribute means, you can't write a good query. And if an AI agent doesn't know what it means, it guesses.

Are your AI systems giving answers your teams cannot trust? Most enterprises deploy LLMs expecting reliable outputs, but the results often feel inconsistent or incomplete. The problem is the missing structure behind it. Enterprise data is usually fragmented across multiple systems, teams, and tools. Your AI does not understand how customers, products, policies, and operations connect. Without that context, it fills gaps with assumptions, which leads to unreliable results.

Have you tried using AI at work and felt it works well for small tasks, but not beyond that? It can handle simple things like creating a summary, writing a draft, or answering a question. This works because the task is clear. But most tasks are not that simple. They involve multiple steps. One step depends on another. Data comes from different systems, and some decisions need checks before moving ahead. This is where a single AI system starts to struggle.