Latest blog posts from Honeycomb.
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It's been a year since Honeycomb issued its AI mandate. Charity reflects on what that produced, why AI isn't special (it just amplifies what's already there), and shares the first of three new documents on Honeycomb's AI norms and values: how we do business.
A remote engineering manager on why she didn't write a new AI policy for her team. Instead, she created space: for connection, for collaboration, and for discussion.
Learn what AI model drift is, why it happens, and how production teams detect changes in model quality, inputs, prompts, and behavior.
Every BubbleUp query now surfaces significant correlations based on relevance, not just statistical analysis. Available today to all Honeycomb customers who have enabled Honeycomb Intelligence.
We couldn't get through every question during our live AMA with the authors of Observability Engineering, so Charity, Liz, George, and Austin stuck around to answer more on AI, telemetry, and what still needs a human in the loop.
LLMs have reshaped the day-to-day work of software engineering, leaving senior engineers exhausted by context-switching and junior engineers unsure how to grow. The fix isn’t a better prompt — it’s spending more time talking to humans: reducing context churn, pairing across seniority levels, and communicating more across teams.
For the third consecutive year, Honeycomb has been named a Visionary in the Gartner® Magic Quadrant™ for Observability Platforms. The recognition reflects Honeycomb's vision for fast, flexible, high-cardinality querying, agent-era observability with Agent Timeline and Canvas, and predictable event-based pricing at trillions of events.
Faced with an endless stream of AI-generated code reviews, our team made the counterintuitive choice to lean into the bottleneck rather than reduce it. Surprisingly, velocity held up, knowledge sharing improved, and we developed a collective system ownership that stuck.
A year ago, I predicted ways in which AI was about to fundamentally change observability as we knew it. Here's what we've seen happen since—both at Honeycomb and with our customers—and what we're building for the future.
As the Honeycomb engineering team worked to double our productivity, we learned a lot. The most important takeaway? Nothing anyone tells you about AI will land if your starting substrate is unhealthy.