Operations | Monitoring | ITSM | DevOps | Cloud

AI has moved to be a core part of enterprise infrastructure. GPUs are the engines behind that shift. Every training run, every inference request, and every fine-tuning job depends on GPU chipsets that are expensive and delicate. A GPU that overheats, runs out of memory, or sits idle for hours doesn't just slow a project down, it quietly drains the IT budget. Most monitoring tools weren't built with this hardware in mind. This leaves AI and DevOps teams blindsided when a job fails or a chipset degrades.

When Citrix introduced License Activation Service (LAS), customers transitioned from the traditional file-based licensing model to a new cloud-connected licensing architecture. For most administrators, the migration itself was straightforward. The biggest surprise came afterwards, when familiar licensing metrics such as licenses in use, licenses available, and peak license usage disappeared from the Citrix License Server. The answer is no.

We’re making it easier to organize your monitors from the moment you add them. Previously, clicking Add would immediately add a monitor to your board. If your board already contained one or more groups, new monitors were automatically placed in the Ungrouped section, requiring an extra step to move them into the correct group. Now, clicking Add opens a list of your monitor groups, allowing you to choose exactly where the monitor should be added.

In this series, we have talked about how generative AI is shifting the landscape of software creation. In The New Software Creator, we explored how AI expands who can write code. In When Anyone Can Build Software, Deployment Governance Is What Keeps It Safe, we looked at why the deployment pipeline is the ultimate control point. Finally, in Security at Scale: What Changes When Everyone Can Deploy, we dug into the technical realities of patching, container hygiene, and identity management.

Summary The InfluxDB satellite telemetry demo is a live mission-control application that monitors a simulated fleet of 12 satellites in real-time. It shows how the InfluxDB 3 Processing Engine can detect anomalies as data is written, enrich time series data with third-party data, and power a grounded AI agent using the InfluxDB 3 MCP server to investigate and explain fleet health.

Let’s say you want a Matches condition that picks up “prod”, “PROD”, and “Prod”, so you write ‘Matches: (?i)prod’ and check it in an online regex tester before publishing. It looks fine. Python accepts it, and so does JavaScript. Dimension Studio used to accept it too, and the rule is published. Then your data stops updating. The new pattern is what’s keeping it from materializing, but nothing tells you that.

AI is set to transform our technology landscape. For many of us working in software, it already has — developers are now writing more code, building more features, and deploying more applications, faster. For the teams supporting IT and software services that means more applications to support, across a greater breadth of technologies, and with more complexity (that is probably less well understood by the developers who created it). Your operational tooling needs to keep pace.