Operations | Monitoring | ITSM | DevOps | Cloud

Turns out the scariest thing about SSL certificates isn’t when they expire. It’s when they don’t. I wrote about the CA/Browser fight that led to the 47-day certificate mandate. CAs crying about lost revenue, browsers flexing their root program authority, enterprises stuck in the middle. But nobody talks about the security research that started it all: BygoneSSL at DEFCON 2018. Two researchers mining Certificate Transparency logs found something surprising.

Harness Database DevOps now supports CockroachDB, bringing CI/CD automation to distributed SQL databases. Teams can manage schema changes through Git-driven workflows for consistency, traceability, and rollback safety. This integration simplifies multi-environment deployments, reduces human error, and accelerates database delivery.

One of the most challenging aspects of running a SaaS business is dealing with highly variable costs. Every service you provide needs resources, and the demand for those resources fluctuates. Just when you think you’ve got this quarter’s budget nailed down, an engineer makes a restructuring decision or a new feature launch exceeds expectations (or falls flat). Suddenly, your bill looks totally different than what you were expecting.

Every law firm I meet can explain its information security policy in minutes. Far fewer can tell me which AI tools their staff actually used last week, and what data those tools touched. That gap is where Shadow AI sits, such as unsanctioned, unmonitored use of generative AI slips in. It promises speed, but it quietly creates exposure: confidentiality breaches, weak auditability, and a risk to governance when the regulator (or a client’s GC) asks hard questions.

Platform teams handle cluster provisioning manually because it requires oversight. A product team needs a cluster, so they submit a request. Someone reviews the configuration, provisions it through the cloud provider, waits for it to spin up, and sends back access details. Two days later, the cluster is ready. The delay is intentional. Without review, teams provision oversized clusters that blow budgets, misconfigure networking, or deploy in the wrong regions.

Let’s be honest. A lot of AWS customers are still running on-prem GPU servers. Sometimes it’s for internal model training jobs, sometimes it’s cost-sensitive work that doesn’t need cloud-scale reliability. The pattern is common, especially in R&D-heavy environments. The usual go-to is virtualization platforms. But those add complexity and licensing overhead most teams would love to ditch. So, I went looking for something cleaner.

AWS did not intend to build Amazon EKS; it simply had to. Kubernetes adoption beamed light years ahead of AWS’s own managed container orchestration service. This forced AWS to develop a managed service to accommodate customers who wanted to use upstream Kubernetes but did not want to manage it themselves. As soon as AWS got around to it, it knocked the Kubernetes-based container management service out of the park. Not only is Amazon EKS simpler than Kubernetes, but EKS pricing may also be worth it.

At Civo Navigate London 2025, we hosted a panel discussion with Josh Mesout, James Faure, Abdul Hummaida, Jonas Vermeulen, and Daniel Miodovnik to discuss the latest trends and challenges in AI adoption. Through this conversation, the panelists covered topics such as the current state of AI adoption to the challenges of scaling AI, and the future of work.

In a microservices architecture, a single user request can pass through multiple services before completing. When performance drops or an error occurs, tracing that journey is the only way to locate the source. Distributed tracing provides that visibility. At its core are OpenTelemetry Spans — units of work that capture what each service does during a request.