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The latest News and Information on Observabilty for complex systems and related technologies.

How to Make AI-Generated Code Reliable with Runtime Context

AI coding assistants like Cursor and Claude Code are driving massive productivity gains, yet they have introduced a critical validation gap in the software delivery lifecycle. While these tools excel at generating syntax, they lack visibility into live production environments. This article explains how Runtime Context, the missing nervous system of AI development, secures production by moving from probabilistic guessing to deterministic, live code validation.

Teaching AI How to Refinery

At the beginning of February, we released v3.1 of Refinery, our advanced, tail-based sampling solution. The new version comes with more performance enhancements, bug fixes, and a few new pieces of telemetry. In tandem with the 3.1 release, we also released a new tool for our MCP server which helps your AIs understand Refinery, and how Honeycomb handles sampling.

Introducing "Explain Flame Graph": Stop Fighting Fires and Start Explaining Them

In a modern observability deployment, it’s simple to get data that helps you understand where your system is failing. However, when we try to understand why, the answer is often buried beneath a mound of stack traces. For many developers, attempting to interpret a flame graph by manually calculating self-time (the resources consumed by the function itself) versus child-frame latency (the time spent waiting on called sub-functions) is both confusing and time-consuming.

Sovereign observability: How UAE data residency powers resilient digital economies

Cloud observability is a must for IT teams operating in modern digital economies. It allows administrators to see inside complex systems, understand how each component behaves under real conditions, and act before users or regulators feel the impact. In simple terms, observability transforms digital infrastructure from a black box into a transparent, accountable, and resilient system.

Uptrace Errors & Logs Tutorial: Capture Stacktraces, Context, and Traces in One Place

Every error tells a story — and Uptrace helps you see the full picture. In this tutorial, you’ll learn how to use Uptrace to capture errors, logs, stacktraces, and request context in a single observability platform. See how errors automatically link to traces, understand exactly what happened, and debug issues faster with rich attributes, user data, and performance impact. What you’ll learn: Understand not just *what broke*, but *who it affected and why* — and fix problems with confidence using Uptrace.

Uptrace Tutorial: Dashboards, Percentiles, Heatmaps & OpenTelemetry Metrics

Learn how to use *Uptrace* to measure what truly matters in your applications using percentiles, heatmaps, and histograms—then turn that data into dashboards that answer questions before they’re even asked. In this tutorial, you’ll discover how to: Whether you’re setting up observability for the first time or replacing expensive monitoring tools, this guide shows how Uptrace helps you understand performance, reliability, and user experience — all in one place.

End-to-End Tracing with Uptrace: Follow Any Request Across Your Entire System

Stop guessing where requests slow down. With Uptrace, you can follow any request across your entire system and instantly see performance bottlenecks, errors, and latency sources. This video covers: Build real observability, not just dashboards.

Happy Birthday to Us: Honeycomb 10 Year Manifesto, Part 1

Christine and I started Honeycomb in 2016, which means it’s been ten years. Christine, a developer, and I, an operations engineer, were both profoundly unhappy with the state of the art in monitoring and logging tools. The tools we had used at Facebook didn’t spray our signals around to a bunch of siloed-off pillars. They consolidated as much context as possible so we could properly explore it, the way every other non-software engineering team already takes for granted.

ilert now supports a native WhaTap integration

ilert now supports a native WhaTap integration, connecting AI-native observability with AI-first incident management in a seamless workflow. This integration allows DevOps, SRE, and IT teams to move instantly from detection to resolution – cutting through alert noise, improving coordination, and dramatically reducing MTTR in even the most complex IT environments.