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.

This post was written by Martin Thwaites and Vivian Lobo. The OpenTelemetry Collector is an exceptional solution for proxying and enhancing telemetry, but it’s also great for generating telemetry from machines too. In this post, we’ll go through a basic, opinionated setup of using the OpenTelemetry Collector to extract metrics and logs from a Windows server.

Logs are more than just records. With proper log monitoring, they become the honey that sweetens observability. Observability is your ability to understand and optimize your system’s behavior. Turning raw logs into actionable insights requires the right tools, practices, and insights. This blog post is a guide on log monitoring key concepts and best practices for sweetening your observability.

You may have wrestled with a web application attempting to call an offsite web service, such as an OpenTelemetry Collector, and gotten an odd error with the word CORS in it. Something like: Or, maybe you got a generic thrown error from your fetch statement that states Error: Failed to fetch …and you wondered, “What’s the problem, and how can I fix it?” These kinds of errors are called CORS errors, and they can be a bit confusing.

Another one in the history books: 2024 is (almost!) over. The OpenObservability Talks podcast, hosted by Dotan Horovits, recently featured a lively discussion with Charity Majors, Co-founder and CTO of Honeycomb, to reflect on the trends, achievements, and future of observability.