Engineering Metrics

Unlike AdTech variables—which are locked into a strict 1-to-4 scale to satisfy Ad Server constraints—the ScaleDynamics Engineering Scores are open-ended and continuous.

These integer metrics are designed to scale upwards, higher is better. As hardware manufacturers release new generations of CPUs, GPUs, and NPUs, the metrics will naturally expand. This provides developers with a highly granular, future-proof benchmark to route workloads with absolute precision.

1. bdp (Browser Device Performance)

The bdp score represents the raw, single-threaded processing and rendering baseline of the machine running the browser. It combines CPU clock heuristics with the browser's JavaScript engine execution efficiency.

• Type: Integer (e.g., 1, 2, 3, 4)
• Scale: Open-ended (Historically starting at 1 for legacy devices, scaling up to 4 for modern high-end desktops/mobiles).
• Primary Use Case: Adaptive UI / Component Loading. Use bdp to decide whether to load heavy visual elements, complex CSS animations, or intensive JS libraries (like Three.js or heavy charting tools).

Scale Breakdown

Implementation Example

async function initializeApp() {
  try {
    const bdpScore = await window.sd.bdp.get();
    
    if (bdpScore > 3) {
      await engageExtendedUI();
    }
  } catch (error) {
    console.warn('Hardware profiling timeout, falling back to safe UI.');
  }
}

2. mdc (Maximum Device Concurrency)

The mdc score defines the absolute maximum number of logical threads the device can safely handle in parallel without starving the main UI thread or causing the browser to lock up. You can see at as the 'true' navigator.hardwareConcurrency that the browser should return to get the maximum processing capacity of the device.

• Type: Integer (e.g., 1, 7, 11, 15)
• Scale: Matches hardware architecture (based on JS thread scheduling limits).
• Primary Use Case: Web Worker Allocation. Use mdc to dynamically size your background worker pools for heavy client-side computations (e.g., data parsing, state management, complex search filtering), or configure the navigator.hwconcurrency.

Implementation Example to overload navigator.hardwareConcurrency

window.sd.mdc.on((mdc) => {
  Object.defineProperty(navigator, 'hardwareConcurrency', {
      get: function() {
        return mdc; 
      },
      configurable: true
    });
});

3. air (AI Readiness Index)

The air index is a composite heuristic specifically designed for Edge AI and machine learning workloads. It evaluates the presence of hardware acceleration (WebGL/WebGPU contexts, NPU signatures).

• Type: Integer (e.g., 1, 2, 3, 4)
• Scale: Open-ended (Historically starting at 1 for basic AI support devices, scaling up to 4 for handling heavy LLMs.
• Primary Use Case: Smart Inference Routing. Use air to determine if a device can safely download and execute local models (via Transformers.js or ONNX Runtime Web) without triggering an Out-Of-Memory (OOM) tab crash.

Scale Breakdown

Implementation Example

async function optinalAIModelLoad() {
  const aiReadiness = await window.sd.air.get();
 
  if (aiReadiness > 3) {
    await loadAndRunAIModel();
  }
}