At scale, understanding performance in a distributed system means seeing beyond metrics and into real production execution.
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Agentic Workflows: What They Are and How to Ship Them to Production
Today AI coding assistants are widely used on a daily basis for software development. They help developers write code and understand it, generate tests, and review changes with…
AI in the Trenches: What We’re Really Learning About Coding with Machines
May Walter reflects on the real lessons from InfoQ’s “AI in the Trenches” panel – where the future of software development is already unfolding.
Production-Driven Code Navigation
Every engineer knows how code is written, but almost none truly see how it flows in production – Hud’s Snaky Chart changes that.a
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Human & artificial alike.
Hud runs on millions of services across massive production environments, with negligible overhead.
“Hud eliminated our voodoo incidents. AI-powered root cause analysis turned days of drilling through tools into minutes.”
“Hud completely changed how early we detect, and how quickly we solve issues long before they impact the business.”
“AI helps us move faster only when it’s grounded in reality. Having accurate runtime context means our engineers, and our AI workflows, can reason about real production behavior and act on it safely.”
“We knew the crashes were caused by an ELU spike, but the question was what caused that spike. Using Hud, we found the answer in minutes with an AI-ready fix.”
“When engineering decisions are grounded in real execution data, it changes how confidently we ship, scale, and invest. That level of certainty directly supports the business as the platform continues to grow.”
