Operations | Monitoring | ITSM | DevOps | Cloud

How many machines in your environment are running software with a publicly documented security flaw right now? That figure comes from an asset inventory, and asset records age quickly once they are written. The gap is rarely about tooling budgets. Software inventory across a few hundred endpoints shifts every week, while the published catalogue of flaws in that software grows every single day. Manual inspection loses that race inside the first month.

Bring network provisioning into your workflow with Megaport CLI, built for repeatable changes from the terminal, scripts, and CI/CD pipelines. When everything runs through a pipeline, changes are reviewable, repeatable, and consistent from staging to production. Your application code ships that way. Your cloud resources provision that way. Then you need a Megaport connection. So you open the Portal, and step outside the pipeline.

Migration projects fail in a predictable sequence. The technical work gets scoped. The timeline gets set. The engineering team starts moving workloads. Somewhere in the middle, dependencies surface that weren't in the original assessment, the double-run period extends beyond the budget allocated for it, and the project either stalls or completes at significantly higher cost than planned.

Server monitoring is still one of the most important disciplines in IT operations because servers remain central to application performance. That is why server monitoring tools remain essential for maintaining infrastructure health, ensuring application availability, and delivering consistent user experiences across modern IT environments.

We’ve previously written about how deeply embedded data is in people’s day-to-day work at incident.io, and I’d have it no other way — demand for data is undoubtedly a good thing. What risks breaking at scale, however, is everything downstream of that demand: data-team capacity gets stretched thin, dashboard sprawl outpaces anyone's ability to maintain it, and stakeholders can't reach an answer without going through the data team.

A few months ago, I noticed something happening. I would spend all day working with LLMs—prompting them, reviewing their work, and correcting them—and when I wasn’t working on my own code, I was reviewing LLM-generated code. By the end of the day, I was exhausted. This was a very unusual thing for me: I’ve been a software developer at startups for 30 years, and while sometimes I might have gotten stressed out, I had never been exhausted by the actual act of writing code.