Operations | Monitoring | ITSM | DevOps | Cloud

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.

When designing effective error handling for React apps, the troubleshooting information you collect and display is critical. React errors can stem from a variety of causes, including user misconfiguration, backend and network issues, and mismatches in browser environments. Instrumenting your code to log critical context, including feature names, user data, and session activity, enables you to quickly identify where these errors originate.

We run Postgres under an operator for automatic failover. That is a promise about what happens during a failure, so the only way to know you have it is to cause the failure and watch. The docs tell you what should happen. A config review tells you which knobs are set. Neither tells you how long an isolated primary keeps accepting writes after its replacement has been promoted, and that number decides whether a failover is clean or leaves you with two versions of your data.

Enterprise AI agents clear every demo and pilot, then hit a compliance wall. The gap isn't technology—it's architecture. Discover why governance must sit inside the execution flow across six control points, not bolt on afterward as an afterthought at input and output only.

How to recover deleted OneDrive files: the recycle bin, the 93-day window, restoring your whole OneDrive, and how admins get back a departed user's files.

Model Rightsizer is an open-source Claude Code sub-agent from CloudZero that scores each task on capability need versus cost pressure, then routes it to the smallest model that can handle it. In its first week, it cut Opus spend 75% while shifting 234x more work to Sonnet. Every Claude Code agent you run has to answer a question it usually never gets asked: does this task need the smartest model available, or are you paying Fable prices to rename a variable across three files?

Only 22% of finance leaders can tie their AI spend to a business outcome, according to CloudZero’s 2026 finance survey. When AI ROI falls short, it usually isn’t because a company is doing too much AI. It’s that no one is watching which model runs which task, and that one choice accounts for a large part of the cost. Here’s why it happens.