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.

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. AI tools, and especially LLMs, are growing incredibly quickly, and I feel that these tools have a ton of potential.

As the Product Manager for Honeycomb’s new frontend product, Honeycomb for Frontend Observability, I’ve had the joy this past year of speaking to dozens of frontend engineering teams about observability. Many frontend teams come from worlds where they either rely on QA and customer reports to identify issues in production, or they use real use monitoring (RUM) and error monitoring tools to catch the most egregious issues.

We love to talk about the value of observability in accelerating feedback loops by enabling teams to understand what changes they need to make to software. But a barrier that often holds teams back from completing the feedback loop is how long it takes to actually get feedback on code under development, or push code into production.

Gearset has been revolutionizing Salesforce DevOps since its founding in 2015. The Cambridge-based team set out with a clear mission: to make Salesforce deployments simpler, faster, and more reliable for every team. Today, Gearset’s powerful product suite is trusted by over 2,500 companies worldwide to deploy metadata, automate CI/CD pipelines, seed sandboxes, and secure critical customer data.

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 GitHub Copilot comes in: making tasks that once felt monotonous or time-consuming into faster, more intuitive processes.

The Collector is one of many tools that the OpenTelemetry project provides end users to use in their observability journey. It is a powerful mechanism that can help you collect telemetry in your infrastructure and it is a key component of a telemetry pipeline. The Collector helps you better understand what your systems are doing—but who watches the Collector? Let’s look at how we can understand the Collector by looking at all the signals it’s emitting.