Charity Majors
Observability & operations
CTO of Honeycomb. Observability, platform engineering, and what AI does to operations.
Recent activity
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Years ago I predicted that columnar storage would remake observability. What I didn't see coming: vendors would build it, nerf it, and sell a worse version back to you as "Datadog, but cheaper"
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Implementing AI mandates can be an effective way to fund coordinated change on a tight timeline, acknowledging that some deadlines and standards will slip. A mandate is a forcing function for identifying a timeline and figuring out what kind of enablement is needed, and it forces managers to have hard conversations about tradeoffs and success metrics. A mandate should be backed by a clear plan and resources to support the change. AI summary
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The author argues that AI is not inherently evil or uniquely damaging, but rather a tool that requires responsible use and engagement to mitigate its harms, which can be categorized into two buckets: those caused by AI's creation and those enabled by its use. This approach emphasizes the importance of becoming experts in AI and contributing to its development to shape its future. AI summary
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If you lived through the shift from handcrafted server pets to immutable infrastructure, you should sense something oddly familiar about what's happening now.
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Both sides are grappling with a real existential threat, and both sides feel like they are screaming into the void. Here's how to close the gap and get everyone pulling in the same direction.
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Most software engineers treat observability as a reactive tool, rather than a proactive feedback loop, due to the dominant three-pillars model for telemetry collection, which separates signals into distinct categories and destroys relationships between them. This model makes data exponentially more powerful with each additional attribute, but also limits its ability to support agentic validation and joins across data siloes. AI summary
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One year ago, Fred Hebert and I delivered the closing keynote at SRECon25. Looking back on it now, I can hardly connect with how I felt then. Here's what I'd say one year later.
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I'm not sure whether to say "thank you" or "HOW COULD YOU DO THIS TO ME", but this one goes out to all the people who sent me advice on buying software last fall.
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90% new material, a clearer mission, and a rogues gallery of contributors. The second edition of Observability Engineering is almost done.
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"Operations" is not a dirty word, a synonym for toil, or a title for people who can't write code. May those who shit on ops get the operational outcomes they deserve.
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