Earlier this month, we released the first version of our new natural language querying interface, Query Assistant. People are using it in all kinds of interesting ways! We’ll have a post that really dives into that soon. However, I want to talk about something else first. There’s a lot of hype around AI, and in particular, Large Language Models (LLMs).
The pressure on today’s development teams is real: innovate, release quickly, and then do it all again, only faster. Is it any surprise that studies are showing 83% of software developers are feeling burnout? We’re here to remind you that it doesn’t have to be this way.
Honeycomb recently released our Query Assistant, which uses ChatGPT behind the scenes to build queries based on your natural language question. It's pretty cool. While developing this feature, our team (including Tanya Romankova and Craig Atkinson) built tracing in from the start, and used it to get the feature working smoothly. Here's an example. This trace shows a Query Assistant call that took 14 seconds. Is ChatGPT that slow? Our traces can tell us!
There’s a reason everyone dreads debugging, especially in today’s complex cloud systems: it’s at the high stakes nexus of nervous senior management, overworked engineers, neverending rabbit holes, copious buckets of time, and fickle customers.
The Honeycomb design team began work on Lattice in early 2021. Over several months, we worked to clean up and optimize typography, color, spacing, and many other product experience areas. We conducted an extensive audit of all components, documenting design inconsistencies and laying the foundation for a sustainable design system. However, a more extensive evaluation and audit were necessary before updating or developing components.
Wikipedia defines smoke testing as “preliminary testing to reveal simple failures severe enough to, for example, reject a prospective software release.” Also known as confidence testing, smoke testing is intended to focus on some critical aspects of the software that are required as a baseline.
A while ago, we added Metrics to our observability platform so teams could easily see system information right next to their application observability data—no tool or team switching required. So how can teams get the most out of metrics in an observability platform? We’re glad you asked! We had this conversation with experts at Heroku. They’ve successfully blended metrics and observability and understand what is most helpful to know.
Dear Miss O11y, I remember reading quite interesting opinions from you about usage of metrics and traces in an application. Did you elaborate on those points in a blog post somewhere, so I can read your arguments to forge some advice for myself? I must admit that I was quite puzzled by your stance regarding the (un)usefulness of metrics compared to traces in apps in some contexts (debugging).
It’s 5:00 pm on a Friday. You’re wrapping up work, ready to head into the weekend, when one of your high-value customers Slacks you that something’s not right. Requests to their service are randomly timing out and nobody can figure out what’s causing it, so they’re looking to your team for help. You sigh as you know it’s one of those all-hands-on-deck situations, so you dig out your phone and type the "going to miss dinner" text.
You know the old saying, I’m sure: “April deploys bring May joys.” Okay, maybe it doesn’t go exactly like that, but after reading what we’ve been up to for the past month, I think it might be time for an update. Let’s dive into our Feature Focus April 2023 edition.
