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Hud: Runtime Code Sensor for Production-Safe AI Code

AI Summary4 min read

TL;DR

Hud is a runtime code sensor that captures detailed execution context for AI-generated code in production. It identifies errors and performance issues by analyzing code behavior, not just logs, enabling AI to understand and fix problems effectively.

Key Takeaways

  • •Hud provides deep runtime context for AI-generated code, capturing execution paths, variable states, and side effects to make AI production-safe.
  • •It solves the problem of limited monitoring tools by turning executable code into an observable system, helping identify errors like timeouts and regressions.
  • •The tool enables continuous runtime intelligence, allowing AI to understand and correct errors before execution, improving debugging and performance optimization.

Tags

webdevjavascriptprogrammingopensource

Today, every modern application is increasingly shaped by artificial intelligence. Production systems now use AI for their logic, but the AI that makes these systems can't really see what's happening when they're running.

From what I've seen, most monitoring tools have a big problem. They don't give enough detailed info about what's going on when the code runs. This makes it hard for AI to really get what the production code is doing.

This post will talk about Hud. It's like a sensor for code when it's running. It spots mistakes and slowdowns in production and gives the detailed info needed to fix them with AI.

Well, let's get started!

👀 What is a Hud and what problem does it solve?

First of all, it's worth understanding that this module is not just an extension for visualizing errors in the IDE and is not a monitoring system in the classic sense. It's more than that. It's an architectural layer that turns your executable code into an observable system.

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It works not with logs and metrics as usual, but with the behavior of the code at runtime, capturing the context that is usually lost between writing the code and its actual behavior in production.

Check out Hud

✅ The runtime context AI-generated code is missing to become production-safe

AI today can write syntactically correct code, but it does so in the context of certain abstractions, and not in the context of actual code execution.

Here's a js example of how it works:

Code

function fetchData(url) {
  const response = httpGet(url)
  const data = JSON.parse(response)
  processData(data)
}
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Runtime Context

{
  "function": "fetchData",
  "inputs": {
    "url": "http://localhost:3000/users"
  },
  "execution_path": [
    "httpGet()",
    "JSON.parse()",
    "processData()"
  ],
  "side_effects": {
    "httpGet": "timeout",
    "processData": "not_called"
  },
  "error": {
    "type": "TimeoutError",
    "message": "Request exceeded 3000ms"
  }
}
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This means that without a runtime context, at first glance our code seems to work, but in fact the executable code produces an error.

⚙️ Runtime Code Sensor: How Hud sees code behavior in production

Returning to our example, let's consider the following. Imagine our code is a large object.

We can, of course, look at how a value is written using eslint, but the Hud itself is more than just that: it includes execution paths, variable state, side effects, and much more.

CallGraph

In this way, Hud creates deep context that allows you to understand why an error occurred and how to fix it.

🌐 From observability to continuous runtime intelligence

This layer builds a bridge between regular observability, which shows the result as an error, but does not show why exactly the code led to this.

Error

In this example, we can clearly see that the image is causing an error, but where? And most importantly, if the component is large, how can the AI understand what's causing it without clear context?

A real-time context is created specifically for production, which can be read by AI, and in your application you can correct an error before execution, say, on a website.

🔎 Finding regressions and failures without logs and dashboards

Instead of just logging errors and weird stuff, we record them as structured events while the program is running. This helps you:

  • Find the root cause of issues faster;

  • Get why things went wrong and what happened after that;

  • Spot sneaky problems that regular metrics and logs miss.

The Runtime Code Sensor makes every bit of code that runs a source of info about your system. You can then check this to fix things and improve performance, instead of just guessing or looking at basic data.

📦 Installation

To install Hud on your project, there is detailed documentation described here.

Installation

It will take literally a couple of minutes.

Thank you for reading this article!

You can see other materials on the project here: https://www.hud.io

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