Honeycomb recently released our Query Assistant, which uses ChatGPT behind the scenes to build queries based on your natural language question. It's pretty cool. While developing this feature, our team (including Tanya Romankova and Craig Atkinson) built tracing in from the start, and used it to get the feature working smoothly. Here's an example. This trace shows a Query Assistant call that took 14 seconds. Is ChatGPT that slow? Our traces can tell us!

As the largest liquidity network in crypto, Paradigm facilitates more than $11 billion in monthly volumes, representing nearly 40% global cryptocurrency option flows. Their free-to-use platform provides a single point of access to multi-asset, multi-instrument liquidity on demand, and Software Architect Jameel Al-Aziz leads the team of developers who build and maintain the platform.

Progressive delivery is a modification of continuous delivery that allows developers to release new features to users in a gradual, controlled fashion. It does this in two ways. Firstly, by using feature flags to turn specific features ‘on’ or ‘off’ in production, based on certain conditions, such as specific subsets of users. This lets developers deploy rapidly to production and perform testing there before turning a feature on.

In the rapidly evolving landscape of modern tech, system reliability has become a critical factor for businesses to succeed. To ensure the stability and performance of complex distributed systems, companies are relying on observability—a concept that isn’t synonymous, but instead goes beyond traditional monitoring approaches.

As we’ve shown in previous blogs, Elastic® provides a way to ingest and manage telemetry from the Kubernetes cluster and the application running on it. Elastic provides out-of-the-box dashboards to help with tracking metrics, log management and analytics, APM functionality (which also supports native OpenTelemetry), and the ability to analyze everything with AIOps features and machine learning (ML).

There’s a reason everyone dreads debugging, especially in today’s complex cloud systems: it’s at the high stakes nexus of nervous senior management, overworked engineers, neverending rabbit holes, copious buckets of time, and fickle customers.

We’ve known for years that context switching is detrimental to productivity. Both computers and humans become less productive with each additional concurrent task or priority. Every time you need to shift your focus between projects, you lose approximately 20% efficiency as you figure out where you left off, what needs to be done, how the work fits into the project, etc.

Observability has become a critical aspect of modern software development and operations, allowing organizations to gain insights into the health and performance of their applications and systems. One of the key decisions when implementing observability is choosing between commercial or open-source tools. We spoke to several professionals who shared their experiences and insights on this topic, shedding light on the pros and cons of each approach.

The Honeycomb design team began work on Lattice in early 2021. Over several months, we worked to clean up and optimize typography, color, spacing, and many other product experience areas. We conducted an extensive audit of all components, documenting design inconsistencies and laying the foundation for a sustainable design system. However, a more extensive evaluation and audit were necessary before updating or developing components.

Enterprises have enough data, in fact, they are overwhelmed with it, but finding the nuggets of value amongst the data ‘noise’ is not all that simple. It is bucket’d, blob’d, and bestrewn across the enterprise infrastructure in clouds, filesystems, and hosts machines. It’s logs, metrics, traces, config files, and more, but as Jimmy Buffett says, “we’ve all got ’em, we all want ’em, but what do we do with ’em”.