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.
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.
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. A feature flag lets you deliver different functionality to different users without maintaining feature branches and running different binary artifacts.
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.
Our industry is in the early days of an explosion in software using LLMs, as well as (separately, but relatedly) a revolution in how engineers write and run code, thanks to generative AI. 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.
TL;DR: Sometimes I get hung up in the scientific definition of "experiment." In daily work, take inspiration from it. Mostly, remember to look at the results.
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. Teams can now create Boards based on pre-built templates that generate visualizations with a single click.
This guest post is written by Ian Duncan, Staff Engineer - Stability Team at Mercury. To view the original post, go to Ian's website. 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.
