THE FIRST

Runtime Code Sensor

Hud is a new way to understand how code behaves in production, detecting errors and latency issues with the deep forensic context needed to fix them with AI.

Will we ever trust AI to ship production code?

Engineering teams are under pressure to move faster, ship more, and use AI to do it. But even with the best tools, production still breaks in unexpected ways. Too often, we hear about issues from users before we see them ourselves. Debugging can take hours, often pulling your most senior engineers away from important new work.
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AI should make this better, but when AI writes code without understanding how production behaves, it often makes things worse. Teams want to spend time building new products, not chasing stability issues or reviewing AI’s risky pull requests. Hud changes this. It gives both engineers and AI real visibility into how code behaves in production, so they can detect problems earlier, find the real root cause instantly, and fix them safely.

Introducing Hud

Engineering teams are under pressure to move faster, ship more, and use AI to do it. But even with the best tools, production still breaks in unexpected ways. Too often, we hear about issues from users before we see them ourselves. Debugging can take hours, often pulling your most senior engineers away from important new work.

With Hud, teams focus on building, not debugging

Detect, understand, and fix errors & performance degradations before users notice.

Skip the time-intensive bug-hunting process. The root cause is already known.

Use AI to safely verify and fix production issues.

Know instantly whether a new release introduced regressions, and why.

See code in a different way

The Hud difference

Hud is fundamentally different from legacy observability, APMs, and error trackers. It is based on the Runtime Code Sensor, a lightweight component that installs in minutes and runs alongside your code with negligible footprint.

See code in a different way

Ubiquity

Hud lives with every piece of code - if it runs in production, Hud knows how it behaves, whether it’s failing, and whether it’s getting better or worse over time.

Dynamic depth / lower cost

Hud continuously captures lightweight signals about runtime behavior, and when something goes wrong, it automatically collects the forensic context needed to understand and resolve the root cause.

Built for AI consumption

Hud’s data model was designed so AI agents can reason over any piece of code with real production context  - enabling safe, confident AI-generated changes.

Under the Hood

Hud’s architecture is built around its Runtime Code Sensor - a lightweight SDK-level component that runs alongside application code at the function-level and operates within existing permissions and access controls. The sensor collects aggregated data during normal operation, and deeper forensic data when issues occur, such as errors and performance regressions. All information is processed securely within the user’s environment and sent through the secure protocols to Hud’s core logic, which performs cross-instance correlation and version analysis to identify systemic or environment-specific issues - without requiring direct access to source code or sensitive data.