Observability and monitoring are not about gathering different data—they differ in their purpose, but share the same data.

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).

f 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.

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.

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.

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. 

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.

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.

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.

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.