Operations | Monitoring | ITSM | DevOps | Cloud

Every day I seem to run into yet another post with someone solemnly opining that “writing code has never been the hardest part of software engineering. And hey, that’s smashing. As an engineer from the ops/infra/SRE side of the house, I feel like I’ve been saying this my whole career. (Is there anything more satisfying than being proven right in public? Not in my book.) So, which is it?

You don’t need to be a power user to get powerful results. I’m not training models or prompting GPTs into poetry—I’m just using them to do what great managers already try to do: communicate clearly, prioritize outcomes, and lead with intention. Over the last few quarters, I’ve built a handful of custom GPTs to support my weekly, monthly, and quarterly workflows.

If you’re unfamiliar with AWS Summits, these are conferences that occur on a yearly basis in different cities. The events are mostly used to announce new products and technologies. This year, the theme was AI, as evidenced by the keynote, a large majority of the talks, and a walk around the vendor floor. The keynote talk was hosted by Swami Sivasubramanian, VP of Agentic AI at AWS.

Some people can get an AI assistant to write a day’s worth of useful code in ten minutes. Others among us can only watch it crank out hundreds of lines of crap that never works. What’s the difference? There are some skills specific to AI development. There are also properties of the codebase we’re working in that make it amenable to AI assistance. Most AI demos use projects created from scratch with AI in mind—cute.

In the era of AI, software development is at an inflection point, and observability has never been more critical. Teams are dealing with more code, more data, and more pressure than ever before. To navigate these new challenges, you need a partner with a strong vision for the future and a knack for looking around corners. Honeycomb is proud to be named a Visionary in the 2025 Gartner Magic Quadrant for Observability Platforms.

I’m pleased to announce the public beta of Honeycomb Hosted MCP, along with our first wave of one-click integrations for Cursor, Visual Studio Code, and Claude Desktop. We’re also very excited to announce that Hosted MCP is available on AWS AI Agents marketplace and for all Honeycomb plans (including our free plan!) at no charge. Honeycomb was built with a singular focus: how do we help teams become better at the art and craft of software development, delivery, and operations?

Over and over, we’ve seen that teams who invest in adding rich, relevant context to their telemetry end up debugging faster and collaborating more effectively during incidents. Getting meaningful context added can feel like a big cross-team project, but some of the highest-leverage improvements don’t require app code changes or coordination across services.

One of the most common challenges in modern software engineering today is understanding how requests flow through applications. As system architectures shift to favor widely distributed, cloud-native designs, keeping track of how an application processes user actions is more difficult than ever. A single user action may trigger events processed in dozens of backend services. Traces are helping software developers today with this challenge.

When we talk to new Honeycomb users, a few things stand out as sounding downright magical. Sometimes we’ll hear, “Wow, is that a new feature?” and we’ll say that no, it’s been like that for years. Clearly we need to get the word out! This is the first installment of a blog series I’ll be writing, covering areas of Honeycomb that elicit reactions of awe and disbelief from new users.

At LDX3 in London last week, two roundtables I hosted with engineering leaders confirmed what many of us are starting to feel: observability isn’t just important—it’s becoming essential to how modern teams navigate the pressure to move fast and stay resilient.

One of the great things about OpenTelemetry is that it’s a standard, and standards tend to proliferate. I was excited to see Claude Code add OpenTelemetry metric and log support in a recent release. What was really interesting—beyond the ability to capture usage data from Claude Code—is that you can also get pretty detailed logs about what you’re doing with Claude Code.