Operations | Monitoring | ITSM | DevOps | Cloud

The financial service industry has spent years modernising their software delivery pipelines. Build and test cycles are fast, infrastructure is automated, and engineering capability is no longer the bottleneck. The slowdown now occurs at the end of the process: the last mile, where a change must prove it is safe before it can enter production. This final step is governed by a trust layer with people in it.

Start with distributed tracing to find which service is slow, then use continuous profiling to see why the code is slow, and finally apply high-cardinality analysis to identify which users or conditions trigger the problem. It's 2 AM. Your phone buzzes. Users are reporting timeouts. The metrics dashboard shows p99 latency spiking from 200ms to 4 seconds, but everything looks normal—CPU at 60%, memory stable, no error spikes. A quick pod restart helps briefly, then latency climbs right back up.

In my previous posts, we discussed the where and the how of managing your ML assets. We showed you how JFrog Artifactory acts as a powerful, universal model registry (the “where”) and how the FrogML SDK serves as the gateway to get your models and metadata into it (the “how”). Now, let’s talk about the why.

At some point in the last few decades, every enterprise convinced itself that the humble FAQ page was going to save IT. If you could just document everything (every how-to, every troubleshooting step, every tribal data nugget living in someone’s head) you could finally stop the ticket flood. The idea was for employees to self-service and avoid escalating to engineers while freely sharing knowledge across a de-siloed ecosystem. But of course, that’s not what actually happened.

Stop guessing on AI hardware. This DevOps guide details when to use TPUs vs. GPUs for optimal performance, cost, and framework compatibility in MLOps.

Traditional testing methods often delay the software development lifecycle, as we have grown used to these outdated processes without considering alternatives. Ephemeral environments introduce a more efficient solution. They allow for the quick creation and dismantling of isolated testing environments. These isolated environments approach leads to faster and more productive development cycles while still delivering high-quality software to users.

5G continues to transform the telecommunications landscape, enabling massive device density, edge computing, and new enterprise use cases. However, operators still face significant cost pressures: from accelerating RAN modernization and 5G SA rollouts to energy demands and the shift to cloud-native network functions (CNFs). As telcos redesign their infrastructure strategies, open source has become a key lever to reduce costs, increase flexibility, and accelerate innovation.

AI coding assistants have transformed how developers write and debug code. But there’s a gap: these assistants often can’t see what’s happening in your CI/CD pipelines. When a build fails, you’re still stuck switching tabs, hunting through logs, and copying error messages back into your editor. What if your AI assistant could talk directly to CircleCI? In this tutorial, you’ll learn how to connect Cursor, an AI-powered code editor, to CircleCI using the CircleCI MCP server.