Operations | Monitoring | ITSM | DevOps | Cloud

In our previous post, Navigating the Complexities of Scaling AI in Enterprise Operations, we explored the “cost–human conundrum”, balancing the promise of automation and the realities of economics, skills, and governance. That discussion highlighted a critical inflection point: scaling AI is not just a technical challenge, but an organizational one.

InfluxDB 3 Explorer 1.7 is a step forward for anyone who wants to manage their time series data without constantly switching between the UI and a terminal. This release adds table-level schema management, the ability to import data from other InfluxDB instances, and a new Transform Data section to reshape your data, all within the Explorer UI.

Many BGP route leaks reported by automated detection systems are actually brief, low-impact artifacts of normal BGP convergence. Doug Madory examines examples from Cloudflare Radar, Routeviews, and Jared Mauch’s long-running leak detector to show how these “ephemeral leaks” arise, why they usually don’t disrupt traffic, and why they still matter for routing security.

N+1 query problems are one of the most common, and quietly damaging, performance issues in production applications. One extra query per record feels harmless in development. At scale, it becomes the reason your response times degrade and your database buckles under load. Today, AppSignal adds N+1 detection to its OpenTelemetry support. When we identify the pattern in a trace, we collapse the repetitive spans directly in the timeline, making the problem immediately visible in the trace itself.

Native crashes on Android have always been harder to debug than they should be. The platform has its own crash reporter (debuggerd) that captures the crashing thread, every other running thread, register state, and memory maps into a file called a tombstone. Tombstones have been a part of Android for a long time; in fact, they’ve been there in one form or another since Android's first commit.

AI SREs are autonomous systems that handle incident triage, root cause analysis, and remediation by correlating logs, metrics, traces, and code signals. However, as they rely on pre-configured telemetry, the critical execution details of a specific failure, such as variable state and code paths, can often be missed. As a result, they either force users into manual redeploy loops or make inferences from partial data, diagnosing issues using probability rather than proof.