Operations | Monitoring | ITSM | DevOps | Cloud

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.

Dell OpenManage Enterprise (OME) is a management and monitoring application that provides information about Dell servers, chassis, storage, and network switches across the enterprise network. Sunbird’s new Dell OME connector programmatically pulls data from Dell OME into dcTrack, driving automation and giving you a single pane of glass with complete information across all your Dell assets.

Since Anthropic announced Project Glasswing and the capabilities of Claude Mythos Preview, and OpenAI announced GPT-Cyber – my calendar has looked the same every day: Back-to-back calls with CISOs, AppSec leads, and security architects. And every call starts with the same question.

Together AI pricing ranges from $0.10 to $9.00 per million tokens. Compare all models, GPU rates, free tier details, and practical cost optimization strategies. Written for engineering leads, platform teams, and FinOps practitioners evaluating open-source inference providers.

Grafana Cloud pricing starts free and scales by metrics, logs, and traces. Compare all tiers, real-world costs, managed options, and ways to optimize. Written for DevOps engineers, SREs, platform teams, and FinOps practitioners managing observability spend.

Compare LLM API pricing across OpenAI, Anthropic, Google, DeepSeek, and Mistral in 2026. Full pricing tables, hidden cost breakdowns, and proven strategies to cut AI spend. Written for engineering leads, platform teams, and FinOps practitioners evaluating or optimizing production AI costs.

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.