THE FIRST
Runtime Code Sensor for Code Generating AI
The missing link between LLMs and the real world - streaming live production behavior so coding agents can deliver production-safe code
Production-Aware
by Design
Install Once. No Config. Zero Maintenance.
Hud integrates via a lightweight SDK. No agents, no dashboards, no tuning. From the first run, it captures how code behaves, fails, and scales - delivering the insights that drive everything else.
Designed for AI
Hud gathers runtime context structured on function level, ready to be streamed via MCP for real-time reasoning by AI agents. No instrumentation. No guesswork. Just production truth, delivered when it matters.
The Engine Behind
Production-Aware AI
The First Runtime Code Sensor
A runtime-native layer for production-safe development-lightweight, resilient, and precise. Runs alongside the application to capture live function-level behavior: performance, errors, flows, dependencies and execution paths - no logs, traces, or instrumentation required. Surfaces degradations in endpoints and queue consumers, linking symptoms to exact function-level causes.
Hud’s MCP Server
A structured runtime interface between production systems and code-generating AI. It delivers real-time context - endpoint behavior, failure patterns, and function-level performance - cleanly packaged for Cursor, Copilot, Windsurf, etc... Execution paths, performance shifts, and failure correlations are mapped directly to the context LLMs are reasoning over.
Production-Aware IDE
Hud integrates directly into the IDE, surfacing live production signals at the function level-performance, exception flows, execution frequency-precisely where code is authored. No context-switching. No guesswork. Just immediate visibility into how each unit behaves in real-world conditions.
Trusted by Engineers.
Human & Artificial Alike
Where Code
Meets Reality
Fix Code Based on
Runtime Evidence
When proposing a fix, the agent queries Hud to retrieve production behavior for affected functions: error propagation, caller relationships, execution frequency, and performance data.
This context enables safer and more accurate modifications-grounded in what the system is actually doing, not static code-based assumptions.
Drive End-to-End, Production-Aware Workflows
Hud allows agents to operate with continuous access to production context-through structured, queryable signals captured by Hud’s runtime code sensor.
Every step-issue detection, resolution, validation-is backed by live data, transforming code generating AI systems from speculative copilots into reliable, production-safe collaborators.
Identify Failing Behavior
in Production
Coding agents use Hud’s MCP to detect and investigate production failures, surfacing relevant functions, error sources, and execution patterns-without relying on logs or predefined dashboards. Hud knows the entire codebase, so all the data is right there for the model to reason over.
→ Issues are surfaced with root cause attached
→ Responses include contextual details: behavior trends, exception types, and invocation impact
Validate Code Changes Before They Ship
Before applying changes, the agent validates their implications by querying Hud against current production behavior.
This includes flow participation, load impact, and runtime sensitivity-ensuring that edits align with real-world constraints and won’t introduce regressions.
See Hud in Action
See Code in a New Way
Know first.
The Runtime Code Sensor.