Operations | Monitoring | ITSM | DevOps | Cloud

As AI becomes increasingly central to modern software development, the ability to craft effective system prompts has emerged as a crucial skill. Whether you’re building a code generation tool, creating a chatbot, or developing AI-powered features, your success largely depends on how well you can communicate with AI models through prompts. At CircleCI, we’ve spent countless hours working with developers who are integrating AI into their applications.

Git is a terrific tool that many developers use to keep track of their projects’ versions. Despite the fact that there are many different version control systems, git is by far the most used. The focus on distributed development and the ease with which branches can be used for good reasons.

The Qovery CLI gives developers full control of their infrastructure from the terminal. Whether you're deploying apps, managing environments, or debugging issues, these commands will save you time and clicks.

Not long ago, our co-founder and CTO, Erik Peterson, shared some insights on AI spending. He shared how AI costs currently fall under the write-off-friendly world of R&D. He also acknowledged why DevOps teams might feel it’s too early to start optimizing AI costs. As the saying goes, “Premature optimization is the root of all evil.” But after more than a decade of software development, Erik knows that eventually, research, experimentation, and big ideas need to deliver real returns.

If you are a DevOps manager you will be keenly aware that the role involves managing multiple toolchains across different clouds, platforms and environments. You also need to report on KPIs, DORA metrics, governance, security and a lot more. At SquaredUp, we understand these demands and have developed a suite of plugins and ready-to-run dashboards to help you reduce toil as well as pull all of your key analytics together within a single pane of glass.

When you're running a Java application, the JVM is doing a ton of work behind the scenes but unless you're monitoring those internals, it's hard to know how your app is really performing. JVM metrics give you a window into the heart of the runtime: how much memory you're using, how often garbage collection is kicking in, how many threads are active, and where potential bottlenecks might be hiding.