LLMs

Honeycomb In Your IDE? Yes, With Hosted MCP Now Available in AWS Marketplace AI Agents and Tools Category

Can Claude Code Observe Its Own Code?

Tales From the Trench: Building With LLMs and Honeycomb

It’s The End Of Observability As We Know It (And I Feel Fine)

Beyond Shift Left: Engineering Leaders Increase Speed and Resilience With Observability

MCP, Easy as 1-2-3?

Seems like you can’t throw a rock without hitting an announcement about a Model Context Protocol server release from your favorite application or developer tool. While I could just write a couple hundred words about the Honeycomb MCP server, I’d rather walk you through the experience of building it, some of the challenges and successes we’ve seen while building and using it, and talk through what’s next. It should be pretty exciting, so strap in!

Honeycomb Acquires Grit: A Strategic Investment in Pragmatic AI and Customer Value

We’re excited to share that Honeycomb has completed our first-ever acquisition: we’re joining forces with Grit, bringing aboard not only a strong team but also compelling technology that supercharges our ability to deliver on our mission: to bring observability to every software engineer.

How Does ‘Vibe Coding’ Work With Observability?

You can’t throw a rock without hitting an online discussion about ‘vibe coding,’ so I figured I’d add some signal to the noise and discuss how I’ve been using AI-driven coding tools with observability platforms like Honeycomb over the past six months. This isn’t an exhaustive guide, and not everything I say is going to be useful to everyone—but hopefully it will clear up some common misconceptions and help folks out.

AI for Software Engineering is Just Another “Paradigm Shift”

How many of you started with technologies you still use exactly the same way today? Even if you’re a master Lisp/Clojure developer, you’ve still evolved, right? I should really get my head around those parentheses some day.

Building AI-Ready Infrastructure for the Enterprise

Last week marked the inaugural HumanX conference, a convening of leaders, technologists, policy makers, and media, all brought together to discuss the state of AI and its potential impact on the future of software, business, and society.

LLMs Are Weird Computers

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).

Generating Calculated Fields From Natural Language

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.

Does AI Help Write Better Software, or Just… More Code?

As software teams race to integrate AI into their development workflows, we need to ask ourselves: are AI-powered tools actually making software better?

How I Code With LLMs These Days

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: Where in the Loop Should Humans Go?

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.

AIOps: Prove It!

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.

Using GitHub Copilot to Speed Up Your Development Workflow

As a software engineer, I’m always evaluating tools and technologies that can optimize my workflow. Developer productivity isn’t just about writing more code—it’s about reducing friction, whether that’s context-switching, making repetitive edits, or understanding unfamiliar parts of a codebase. That’s where Copilot comes in: making tasks that once felt monotonous or time-consuming into faster, more intuitive processes.

AI Strategies for Software Engineering Career Growth

All the conditions necessary to alter the career paths of brand new software engineers coalesced—extreme layoffs and hiring freezes in tech danced with the irreversible introduction of ChatGPT and GitHub Copilot. Recession and AI-assisted programming signaled the potential end of a dream to bootcamp-educated juniors.

Observability in the Age of AI

Honeycomb engineers were amongst the earliest adopters of this technology. Not in the widely parodied top-down, VP-mandated, “go be AI leaders nao plz” kind of way, but in a bottoms-up, experimental kind of way, driven by curiosity and fascination.

The Hater’s Guide to Dealing with Generative AI

Generative AI is having a bit of a moment—well, maybe more than just a bit. It’s an exciting time to be alive for a lot of people. But what if you see stories detailing a six month old AI firm with no revenue seeking a $2 billion valuation and feel something other than excitement in the pit of your stomach?