Today marks an exciting milestone at Honeycomb, and we're thrilled to share it with you. We officially launched our integration with Microsoft Teams, a step forward in our continuous effort to streamline and enhance your observability experience. Teams now joins our growing list of over 100 Honeycomb integrations.

Cloud-native developers and practitioners gathered from around the world to learn, collaborate, and network at KubeCon/CloudNativeCon North America 2023 between November 6th and 9th at McCormick Place in Chicago, IL—myself included. This wasn’t my first time attending—I’ve been coming to KubeCon since 2016—but it was easily one of the most exciting experiences I’ve had as part of the Cloud Native community.

ShipHero needed a robust, cost efficient observability platform to support DevOps, customer support, and more. Committed to timely service, ShipHero recognizes that the seamless performance of its software is paramount to customer satisfaction. To maintain this high standard, the development team needs the right data at their fingertips to quickly find and solve problems as they occur.

Now that we’ve had time to decompress from KubeCon, we wanted to do a writeup about our collective experience. Six of us spoke at the conference and Charity participated in a panel, so we included short talk recaps.

So you’ve taken a look at the core web vitals for your site and… it’s not looking good. You’re overwhelmed, and you don’t know what change to make because everything seems like too big of a project to make a real difference. There are so many measurements to keep track of and the standards cited seem even scarier. This is extremely normal. Web performance standards can feel impossible to meet for a lot of us.

Observability is important to understand what’s happening in production. But carving out the time to add instrumentation to a codebase is daunting, and often treated as a separate task to writing features. This means that we end up instrumenting for observability long after a feature has shipped, usually when there’s a problem with it and we’ve lost all context. What if we instead treated observability similarly to how we treat tests?

Ever since we launched Query Assistant last June, we’ve learned a lot about working with—and improving—Large Language Models (LLMs) in production with Honeycomb. Today, we’re sharing those techniques so that you can use them to achieve better outputs from your own LLM applications. The techniques in this blog are a new Honeycomb use case. You can use them today. For free. With Honeycomb.

A lot of reasoning in content is predicated on the audience being in a modern, psychologically safe, agile sort of environment. It’s aspirational, so folks who aren’t in those environments may feel like the path there includes doing “the new thing” or using “the new tool.” If you write software and your employer hasn’t caught up to all the newest, best ways to work, I hope this pragmatic post helps you sleep better at night.

Software systems are increasingly complex. Applications can no longer simply be understood by examining their source code or relying on traditional monitoring methods. The interplay of distributed architectures, microservices, cloud-native environments, and massive data flows requires an increasingly critical approach: observability.

As someone living the Honeycomb ops life for a while, SLOs have been the bread and butter of our most critical and useful alerting. However, they had severe, long-standing limitations. In this post, I will describe these limitations, and how our brand new feature, budget rate alerts, addresses them. We usually don’t have SREs writing product announcements, but I’m so excited about this one that I said, “Screw it, I’m doing it!”