Operations | Monitoring | ITSM | DevOps | Cloud

Oh Dear's dashboard now supports dark mode. You can choose between light, dark, or system-based theming, and your preference is saved to your profile so it follows you everywhere.

Most engineering leaders today understand that diversity matters. They've built teams that reflect a range of backgrounds, functions, and experience levels. They run postmortems, retrospectives, and architecture reviews that bring multiple voices to the table. They believe, not unreasonably, that this variety of perspectives leads to better decisions. But there's a problem hiding inside that assumption that can undermine everything: who people are is a surprisingly poor predictor of how they think.

Writing code is moving from the good old IDE into the realm of autonomous AI agents. One example of this is OpenAI, which has been developing internally with 0 lines of manually written code. You can read about their workflow in their engineering blog: Harness engineering: leveraging Codex in an agent-first world. For me, the main takeaway of OpenAI’s article is how AI has rewritten the constraints equation.

We’ve enhanced our API for custom monitors with more powerful filtering options – giving you better control over how incidents are tracked and surfaced.

APIs power nearly every modern digital experience. From mobile apps and SaaS platforms to payment gateways and internal microservices, APIs handle authentication, transactions, content delivery, and system-to-system communication. When an API fails, users often experience broken features, slow responses, or complete service outages. In many cases, they leave before your team even realizes something is wrong. The business impact of API failures is significant.

APIs are no longer just integration layers. They power customer logins, payment processing, SaaS workflows, partner ecosystems, and mobile applications. When an API becomes unavailable, revenue stops, user trust declines, and service level agreements are immediately at risk. Yet many teams still define API availability in the simplest possible way. If an endpoint responds with a 200 OK, the API is considered available. Monitoring dashboards stay green. Alerts remain silent. Everything appears healthy.

The disconnect between enterprise confidence and AI capability is real. MIT reports fewer than 5% of enterprises have achieved measurable ROI from AI, yet Cisco claims 13% feel ready. The gap isn’t about AI technology—it’s about organizational rigidity and change management. More importantly, most studies focus on business intelligence rather than operational use cases, which are far less risky and more measurable.

As organizations shift away from traditional container engines to embrace Podman’s rootless and daemon-less design, visibility often becomes a challenge. Because Podman doesn't rely on a central background service, traditional monitoring tools can leave you in the dark. Applications Manager's new Podman monitoring feature bridges that gap, giving you total visibility into your Podman workloads without compromising the security model you worked so hard to build.

Splunk is built around a deceptively simple premise: get your data in, search it, and act on it. In practice, the gap between “get your data in” and “data that actually works in Splunk ES” is where most of the engineering effort goes. CIM normalization is non-trivial. Technology Add-on development is slow. Volume-based licensing penalizes growth. And the combination means that as environments expand, Splunk becomes harder to operate efficiently.