Operations | Monitoring | ITSM | DevOps | Cloud

Most PR review tools answer one question: does this code look correct? They scan the diff, flag known patterns, catch security violations, and post inline comments before merge, which is useful, but only half the review. The other half is behavioral: does the code actually behave correctly once it’s running against your live system?

Enterprise applications don't fail quietly anymore. A single checkout flow might touch a dozen microservices, three databases, a message queue, and two external APIs before a customer sees a confirmation screen.

If you run LLM features in production, your most dangerous failures are the ones your monitoring never flags. Your LLM feature passed every test, and the demo went great. Three weeks after launch, a support ticket lands: the chatbot quoted a refund policy that does not exist. The dashboards are all green, and the same prompt answers correctly when you retry it. This is the blind spot LLM observability exists to close. Your existing tools saw the request come back fast with a clean status code.

Like it or not, vibe coding is here to stay. It’s too easy to just go away. Maybe if the per token cost rises too much at some point that it becomes cheaper to hire a junior… But until then, you’d better get used to it. For now, tools like Cursor, Copilot, and Claude let developers (and plenty of non-devs) ship full-stack apps faster than a junior is able to completely grasp the concept of the app they’re working on. And that’s pretty neat.

A year ago, I wrote “It’s the End of Observability (and I Feel Fine).” The upshot of that post was that AI was about to fundamentally change the way we approach systems design and operation in the future. In the grand tradition, I’d like to revisit my claims from then and see how my predictions panned out.

You've optimized your Largest Contentful Paint. Your Time to First Byte is under 200ms. Your Lighthouse scores are green. And yet, your checkout conversion rate is quietly dropping. A segment of users in Southeast Asia is churning. Your support team is fielding tickets about a form that "just doesn't work" and you have no idea which one. Traditional frontend performance monitoring tells you whether your application is fast. It doesn't tell you whether people are actually succeeding when using it.

Cursor was hit by a global “high demand” outage on July 16, 2026, returning ERROR_RESOURCE_EXHAUSTED errors that blocked AI requests for just over two hours. StatusGator caught it early, sending an Early Warning Signal at 06:49 UTC, 12 minutes before Cursor acknowledged the incident at 07:01 UTC. Here is the full picture, including the workaround that kept many users coding.

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we are exploring how prolonged dependence on AI could influence human beings' neural pathways, cognitive habits, and the behavioral changes that follows. As children, many of us would have watched the juggler at a circus in amazement. One ball became two, then three, and several more since it was a cumulative act.