Operations | Monitoring | ITSM | DevOps | Cloud

There are few things that can derail developer productivity and undermine your pipeline like a flaky test. Testing is the backbone of a good development process, ensuring that your code is as accurate and usable as possible. When these tests point towards faulty development, the impacts can be significant. This information is predicated on an assumption, however – the assumption that what the test says is accurate.

When I first started writing Go software a little over a decade ago, one of the features I found particularly intriguing was the ability to build statically-linked binaries for multiple operating systems and architectures without a lot of headache. This build toolchain feature is widely relied upon by nearly all Go developers, especially when needing to build multi-arch container images destined to be run in a Kubernetes cluster consisting of amd64 and/or arm64 nodes.

If you’re leading a technical organization, you’ll have a critical question when you want to perform uptime monitoring: should wse build and run an uptime monitor ourselves, or pay for a service? While it’s totally possible to build your own basic uptime monitor, you’ll end up costing your business more.

In today's connected world, APIs are the backbone of modern applications. Whether you're working on a microservices architecture, a mobile app, or a SaaS platform, APIs are what keep everything talking to each other. But how do you know if your APIs are healthy, performing well, and delivering what your users need? That's where API monitoring comes in. Let's break down what API monitoring is, why it matters, and how you can build effective API metrics dashboards to keep your systems running smoothly.

Generative AI is quickly becoming ubiquitous in the software development space, with tools like Anthropic’s Claude offering rapid methodologies for code iteration, testing, and deployment. As new solutions, such as MCP (Model Context Protocol), are created to make integration more seamless, enterprises are adopting these AI solutions to optimize their development processes, a familiar challenge repeatedly arises: cost.