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.

Space.com sums up the Big Bang as our universe starting “with an infinitely hot and dense single point that inflated and stretched—first at unimaginable speeds, and then at a more measurable rate to the still-expanding cosmos that we know today,” and that’s kind of how I like to think about November 2022 for junior developers.

Have you ever had an alert go off that you immediately ignore? It’s a nuisance alert—not actionable—but you keep it around just in case. Or maybe you’ve looked at a trace waterfall and wondered what exactly happened during a gap that just doesn’t drill down deep enough to explain what’s going on. Do you know the feeling where you have just enough information to monitor what’s going on in your systems, but not quite enough to put your mind at ease?

This post was written by Charity Majors and Phillip Carter. In May of 2023, we released the Honeycomb Query Assistant, an LLM-backed feature that lets engineers use natural language to generate and execute queries against their telemetry data. Instead of having to master a domain-specific query language, you can simply type in things like “slow endpoints by status code” and the Query Assistant will generate a relevant Honeycomb query for you to iterate on.

Refinery is a powerful tail-based sampler—but with great power comes great challenges. We heard your feedback and are excited to announce the release of Refinery 2.9, a rather large update that is packed with goodies to make your life easier when running Refinery in your network.

This post was written by David Chang, Staff Software Engineer at Pinterest, and originally posted on the Pinterest engineering blog on Medium. Develocity, formerly known as Gradle Enterprise, is a powerful tool that speeds up local and CI build time, helps troubleshoot your builds, and analyzes your data. At Pinterest, we have a dedicated team, Mobile Builds, and we ensure that developers can build fast and often. This enables developers to be more productive by getting faster feedback on their code.

We’re always interested in improving the signal-to-noise ratio of our internal telemetry at Honeycomb. In an effort to reduce the amount of noise in our logs, we looked at reducing and deduplicating the logs emitted by our infrastructure and applications.

In the world of digital language learning, Duolingo stands out as a beacon of innovation and user engagement. With millions of users worldwide, their platform is designed not only to teach languages, but also to create a fun and engaging learning experience. Running on the robust AWS cloud infrastructure, Duolingo manages vast amounts of data and user interactions daily. As the company experienced rapid growth, Duolingo remained steadfast in their commitment to delivering a high-quality user experience.

It’s very popular to push logs, in a formatted way, to the console output of an application (sometimes referred to as stdout). Although using a push-based approach like OTLP over gRPC/HTTP is preferred and has more benefits, there are many legacy systems that still use this approach. These systems typically use a JSON output for their logs. So, how do we get these JSON logs into a backend analysis system like Honeycomb that primarily accepts OTLP data?