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Coding agents like Claude Code, Cursor, and Codex CLI handle the coding parts of building an AI application well. The harder work comes after: understanding why a response went wrong, building eval sets that reflect real production behavior, and keeping up with an application that changes faster than any one-off script can. Teams spend 60–80% of their time on evaluation and error analysis, and much of that work needs to be redone every time the stack shifts.

Here's a scenario that's playing out in engineering teams across the industry right now. A developer uses AI to rapidly prototype a microservice. The code works. They deploy it to production. Six months later, something breaks. The system is under load, a database connection pools, and the service starts failing in subtle ways. The engineer pulls up the code, but here's the problem, they didn't write it. An AI assistant did. They don't understand the flow deeply. They don't know where to look first.

You'd think that working at an observability company means everyone knows exactly where to find everything in the data. It doesn't. Especially not on the support team. We're the ones who get the tickets. We're in the telemetry every day trying to figure out what went wrong for a customer, and we do that by pointing Honeycomb at itself. Here's how that actually works, and how it's changed.

Events from Expo apps account for about 75% of the total event volume we receive from React Native apps. That number made it an easy decision to invest in updates to the Sentry React Native SDK to improve the debugging and performance workflow for your Expo apps. With these updates, you can now.

It is 2 AM. Someone on-call gets paged. Conversion rates on the checkout page dropped 30 percent in the last hour. The immediate questions are familiar. Is this a JavaScript error? A slow API call? A broken third-party script? A performance regression that never throws an exception but quietly drives users away? In most teams, answering those questions is not hard because the data is missing. It is hard because the investigation is split across too many places.

The Zephyr west tool is a powerful workspace manager, but it is admittedly pretty feature-rich: there’s a lot of functionality buried inside that tool! I thought it would be interesting to share a few tips and tricks that I use with west to make my Zephyr development workflow a little smoother.

We’ve been quietly solving a problem that most teams haven’t hit yet, but they’re about to. SSL certificate lifetimes are dropping to 47 days. If you’re managing certificates manually today, you have a very short window before that becomes a real operational problem. We know, because it happened to us first.

The most frustrating sentence in modern engineering is no longer "it works on my machine." It is: "It worked in the playground." When an LLM-powered feature, such as a RAG-based search, an autonomous agent, or a dynamic prompt engine, fails in production, it doesn’t throw a standard stack trace. It returns "slop," hallucinations, or silent retrieval failures. Standard debugging workflows fail during triage because LLM hallucinations cannot be reproduced using static mocks or clean seed data.