Large Language Models (LLMs) are all the rage in software development, and for good reason: they provide crucial opportunities to positively enhance our software. At Honeycomb, we saw an opportunity in the form of Query Assistant, a feature that can help engineers ask questions of their systems in plain English. But we certainly encountered issues while building it—issues you’ll most likely face too if you’re building a product with LLMs—with the main one being, how do we get the model to return data in a way that works with our softwa

Before Massdriver, Dave worked in product engineering where he was constantly bogged down with DevOps toil. He spent his time doing everything except what he was hired to do: write software. He founded Massdriver to help engineering teams build and release faster while limiting and managing the complexity of the cloud. 

Who is software for? It’s an interesting question, because there’s an obvious answer. It’s for the users, right? If your job is to write software, then it’s implied that the most important thing you should care about is the experience people have when they use your software. I think this is a bit of an over-simplification, though. Yes, we build software for our users, but we also build it for ourselves. At some level, I believe all developers are in it for themselves. They like to see the thinking rock respond to the commands they give it. There’s a level of intellectual curiosity that grips many of us when we write software, the quiet joy of getting one over on this unfeeling collection of silicon and cobalt, bending it to our will and mastering its arcane language.

Kubernetes has been around for nearly 10 years now. In the past five years, we’ve seen a drastic increase in adoption by engineering teams of all sizes. The promise of standardization of deployments and scaling across different types of applications, from static websites to full-blown microservice solutions, has fueled this sharp increase. 

Understanding production has historically been reserved for software developers and engineers. After all, those folks are the ones building, maintaining, and fixing everything they deliver into production. However, the value of software doesn't stop the moment it makes it to production. Software systems have users, and there are often teams dedicated to their support. 

Like many companies, earlier this year we saw an opportunity with LLMs and quickly (but thoughtfully) started building a capability. About a month later, we released Query Assistant to all customers as an experimental feature. We then iterated on it, using data from production to inform a multitude of additional enhancements, and ultimately took Query Assistant out of experimentation and turned it into a core product offering. However, getting Query Assistant from concept to feature diverted R&D and marketing resources, forcing the question: did investing in LLMs do what we wanted it to do?

Do you want to build software faster and release it more often without the risks of negatively impacting your user experience? Imagine a world where there is not only less fear around testing and releasing in production, but one where it becomes routine. That is the world of feature flags.

Containers are an amazing technology. They provide huge benefits and create useful constraints for distributing software. Golang-based software doesn’t need a container in the same way Ruby or Python would bundle the runtime and dependencies. For a statically compiled Go application, the container doesn’t need much beyond the binary. Since the software is intended to run in a Kubernetes cluster, the container provides the release and distribution mechanism which the Helm chart uses to refer to these binaries. It also allows releasing multiple processor architectures to reference their own images. For general troubleshooting, some pretty good resources exist, like Refinery and the OpenTelemetry Collector.

Many software engineers are encountering LLMs for the very first time, while many ML engineers are being exposed directly to production systems for the very first time. Both types of engineers are finding themselves plunged into a disorienting new world—one where a particular flavor of production problem they may have encountered occasionally in their careers is now front and center.

In high school chemistry and then college physics labs, we learned a strong definition of "experiment." Experiments are tied to the Scientific Method, responsible for advancement of human knowledge.

Whether you’re a new Honeycomb user or a seasoned expert looking to uncover fresh insights, chances are you’ve sent tremendous amounts of data into Honeycomb already. The question is: now what? We have the answer: Board templates. 

At work, we use OpenTelemetry extensively to trace execution of our Haskell codebase. We struggled for several months with a mysterious tracing issue in our production environment wherein unrelated web requests were being linked together in the same trace, but we could never see the root trace span.