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Phillip Carter
The common definition of a computer is a programmable machine that stores, retrieves, and processes data. I would argue that ChatGPT already fits this definition, as you can control how it responds with prompts (programs), it can store data you pass it (memory), it can search that data to generate a response (RAG), and it can process inputs to produce a response (inference).
Molly Stamos
If you’ve been using Honeycomb for a bit, you know that Calculated Fields (otherwise known as derived columns) are a powerful way to transform your events to a format that’s easier to query and understand. However, they use a lisp-esque language that can be difficult to read and a pain to write.
Fahim Zaman
As software teams race to integrate AI into their development workflows, we need to ask ourselves: are AI-powered tools actually making software better?
I first started using AI coding assistants in early 2021, with an invite code from a friend who worked on the original GitHub Copilot team. Back then, the workflow was just single-line tab completion, but you could also guide code generation with comments and it’d try its best to implement what you want.
Fred Hebert
AI is everywhere, and its impressive claims are leading to rapid adoption. At this stage, I’d qualify it as charismatic technology—something that under-delivers on what it promises, but promises so much that the industry still leverages it because we believe it will eventually deliver on these claims.
Rox Williams
OpenTelemetry (often abbreviated as OTel) is the golden standard observability framework, allowing users to collect, process, and export telemetry data from their systems. OpenTelemetry’s framework is organized into distinct signals, each offering an aspect of observability. Among these signals, OpenTelemetry metrics are crucial in helping engineers understand their systems. In this blog, we’ll explore OpenTelemetry metrics, how they work, and how to use them effectively to ensure your systems and applications run smoothly.
Austin Parker
OpenTelemetry is a big, big project. It’s so big, in fact, that it can be hard to know what part you’re talking about when you’re talking about it! One particular critique I’ve seen going around recently, though, is about how OpenTelemetry is just ‘three pillars’ all over again. Reader, this could not be further from the truth, and I want to spend some time on why.
One of the main pieces of advice about Service Level Objectives (SLOs) is that they should focus on the user experience. Invariably, this leads to people further down the stack asking, “But how do I make my work fit the users?”—to which the answer is to redefine what we mean by “user.” In the end, a user is anyone who uses whatever it is you’re measuring.
Ken Rimple
This blog post will get you started ingesting your Next.js application’s telemetry into Honeycomb. I’ll show you the configuration steps, how to view your traces in Honeycomb, and even how to explore your frontend React telemetry with our Frontend Observability Web Launchpad.
Nick Travaglini
Here at Honeycomb, we emphasize that organizations are sociotechnical systems. At a high level, that means that “wet-brained” people and the stuff they do is irreducible to “dry-brained” computations. That cashes out as the inability to ultimately remove or replace people in organizations with computers, in spite of what artificial general intelligence (AGI) ideologues would have you believe. The best that such artifacts can do is “relieve labor-intensive toil,” as my colleagues Charity and Phillip put it.
Martin Thwaites
With more and more people adopting OpenTelemetry and specifically using the tracing signal, I’ve seen an uptick in people wanting to add the entire request and response body as an attribute. This isn’t ideal, as it wasn’t when people were logging the body as text logs. In this blog post, I’ll explain why this is a bad idea, what are the pitfalls, and more importantly, what you should do instead.
In this blog, we’ll share the fundamentals of frontend monitoring, including what you need to know about performance measurement and strategies for staying ahead of monitoring challenges to deliver high-performing, user-centric applications.
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Erwin van der Koogh
One of the hardest parts of my job is to get people to appreciate just how much of a difference Honeycomb/observability 2.0 is compared to their current way of working. It’s not just a small step up or a linear improvement. Rather, it’s an entire step change in the way that you write, deploy, and operate software for your customers.
Back in Alerts Are Fundamentally Messy, I made the point that the events we monitor are often fuzzy and uncertain. To make a distinction between what is valid or invalid as an event, context is needed, and since context doesn’t tend to exist within a metric, humans go around and validate alerts to add this context. As such, humans are part of the alerting loop, and alerts can be framed as devices used to redirect our attention.
Hannah Henderson
There are a limited number of investments that a team can make in any given year and it can be daunting to choose the “right” ones. To simplify our options, I keep coming back to “the future” and “the floor.”
Charity Majors
Ever since January of 2022, we have had an employee representative seated on our board of directors. Paul Osman was the first employee to hold the position; when he left the company, Alyson van Hardenburg took over and filled out the rest of his time; we then nominated her to serve another year in that seat.
In this article, I’ll lay out approaches for wiring Honeycomb to client-side only React so you can ingest your telemetry into Honeycomb and take advantage of the Web Launchpad. This telemetry sends semantically-named attributes, and can be used with any OTLP destination.
Tyler Helmuth
We recently released Refinery 2.9, which came with great performance improvements. Reading through the release notes, I felt the need to write a piece on this improvement, as it’s quite important but easy to overlook: collect loop taking too long. This is the story of how we used distributed tracing to find the slowdown in this loop.
Lex Neva
I’ve read a steadily increasing stream of articles about using AI in SRE, and I have yet to find one that inspires my trust. Each article makes impressive claims about the capabilities of AI and the way it can be applied to SRE tasks, but the vast majority are light on details.
Winston Hearn
In speaking with frontend engineers this past year, I realized that understanding the power of wide events is a big mental shift. We’re used to having metrics—think the P70 of your Core Web Vitals, or the average Time To First Byte (TTFB). These are high-level numbers that give us some insight into the average user experience on your apps—but wide events help us do so much more than metrics can ever dream of.
