Inspecting website signals
Scanning page source, scripts, headers, and asset paths for fingerprint analysis.
Fun fact: repeated template copy and low-trust contact pages are often stronger clues than flashy visuals.
Scanning page source, scripts, headers, and asset paths for fingerprint analysis.
Fun fact: repeated template copy and low-trust contact pages are often stronger clues than flashy visuals.
https://www.linkedin.com/
Scanner classification based on observable evidence
Probably Human-Made at 29% confidence for www.linkedin.com. Based on 1 AI/no-code signals, 8 human-engineering signals, and 13 trust markers.
Human-leaning signal balance 8:1. The scanner considers evidence from multiple independent categories.
Key telemetry from the analysis
1 signal detected
8 signals detected
Named patterns and framework traces
Implementation-level observations
| Attribute | Value |
|---|---|
| HTML payload | 142.2 KB |
| DOM structures | 786 |
| Script assets | 8 |
| Stylesheet assets | 1 |
| Token entropy | 6.99 |
| Repeated text | 0.0% |
| Visible words | 831 |
| Headings | 10 |
| Internal links | 149 |
| External links | 13 |
| Forms | 2 |
| Social links | 1 |
| Policy links | 6 |
| Contact signals | 6 |
| Trust signals | 13 |
The scanner found 1 signal commonly associated with AI/no-code construction, but identified 8 signals associated with manual engineering and 13 trust markers. Because the human-engineering and trust signals outweigh the AI signals, the system classifies this site as probably human-made.
This result does not prove who built the website or whether AI was used during development. The scanner evaluates observable technical and structural signals. A human-built website can exhibit builder-like fingerprints, and an AI-assisted website can contain extensive human engineering.
The scanner evaluates observable technical fingerprints including framework and platform clues, generated or builder-style structures, identifier patterns, metadata quality, structured data, asset characteristics, engineering signals, and trust markers.
No single fingerprint proves AI authorship. The final classification combines multiple independent observations, weighting them against each other to produce an explainable verdict with a confidence score.