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Bringing the Most Advanced Sampling to the OpenTelemetry Collector

Sep 1, 2026 By Mike Goldsmith In Honeycomb
Sampling is a core skill that everyone who runs an observability pipeline at scale will learn. There are lots of tradeoffs within the various decisions you'll make from reducing bandwidth, CPU, and memory, to reducing costs and making the observability backend's performance better for users. Historically, there have only been three mechanisms, each with their own tradeoffs: However, there is a secret fourth option: adaptive tail sampling—which changes those tradeoffs.
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honeycomb

Fin's CTO on Building Great Engineering Organizations in the AI Era

Aug 31, 2026 By Rox Williams In Honeycomb
A few months ago, Darragh Curran, CTO at Fin (formerly Intercom) set a public goal to double engineering productivity and nearly tripled it instead. They did so by pulling a few levers: AI writing code at scale, building an AI-driven PR review system, leveraging observability as a trust mechanism, and with leadership becoming more hands-on through the transition.
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Leading With Observability: Scaling Fin to 2x Engineering Productivity

Aug 27, 2026 By Honeycomb In Honeycomb
A few months ago, Darragh Curran, CTO at Fin (formerly Intercom) set a public goal to double productivity and nearly tripled it instead. They did so by pulling a few levers: AI writing code at scale, building an AI-driven PR review system, leveraging as a trust mechanism, and with leadership becoming more hands-on through the transition. Charity wanted to pick Darragh’s brain on the messy bits, not just the highlight reel, so she invited him to participate in our first episode of Leading With Observability.
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honeycomb

AI Norms & Values, Part 1 of 3: How We Do Business at Honeycomb

Aug 20, 2026 By Charity Majors In Honeycomb
It's been almost exactly one year since we issued our AI mandate here at Honeycomb, and we've been doing some reflection. When we issued our mandate, it's not like we hadn't been using AI. We were the first in the industry to bake a feature powered by AI into our product, way back in May of 2024. Many of us had been experimenting and using these tools in our spare time. But we believe that software is the killer app for AI.
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Signal vs. Spend: Building Cost-Aware Observability at Slack - O11yCon 2026

Aug 20, 2026 By Honeycomb In Honeycomb
It started with a single log line taking up a massive amount of volume: 500 million emissions per hour. Pulling that thread led Emma and Steven into Slack's broader logging pipeline: 311 billion logs per day at 4.4M/sec peak, with no volume limits, no per-service attribution, and no feedback to the teams generating the noise.
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honeycomb

How I Support Humans in the AI Era

Aug 14, 2026 By Ileanell Perez In Honeycomb
When our company pushed everyone to start using AI tools, I thought about what it would mean for my team. As a remote company, we are already challenged by the lack of organic human connection. Every connection is planned and takes effort, and now, AI adds another layer. People now spend part of their day collaborating with a tool rather than with a person, which can take away from the time we spend learning from each other.
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The Three Pillars of Observability: Traces, and Two Things My Agents Never Look At - O11yCon 2026

Aug 14, 2026 By Honeycomb In Honeycomb
'The runbook lost. The trace is the documentation now.' In his O11yCon 2026 closing keynote, Corey Quinn of Duckbill Group makes the case that when your primary reader is an, not a person, are the only pillar built to survive.
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AI Model Drift: How to Keep Models Reliable

Aug 10, 2026 By Dan Juengst In Honeycomb
AI model drift is when an AI system's performance and accuracy degrades over time because the data, user behavior, or business environment has changed since the model was trained or evaluated. Even if latency, uptime, and infrastructure metrics remain healthy, model quality can quietly decline, leading to less accurate predictions, inconsistent responses, and reduced user trust.
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Introducing AI BubbleUp

Aug 5, 2026 By Kale Bogdanovs In Honeycomb
BubbleUp has always been the fastest way to figure out what a group of outliers have in common. Draw a box around a band of slow traces, a cluster of errors, or any set of events you're interested in, and BubbleUp compares that selection to the baseline across every dimension you've sent us. It's how Honeycomb users find the "unknown unknowns" that dashboards can’t show you.
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Signal vs. Spend: Building Cost-Aware Observability at Slack - O11yCon 2026

Aug 4, 2026 By Honeycomb In Honeycomb
It started with a single log line taking up a massive amount of volume: 500 million emissions per hour. Pulling that thread led Emma and Steven into Slack's broader logging pipeline: 311 billion logs per day at 4.4M/sec peak, with no volume limits, no per-service attribution, and no feedback to the teams generating the noise.
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