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://nfl-fan-map.vercel.app
Scanner classification based on observable evidence
Likely AI-Generated at 73% confidence for nfl-fan-map.vercel.app. Based on 4 AI/no-code signals, 0 human-engineering signals, and 3 trust markers across Vercel v0 Candidate.
AI-leaning signal balance 4:1. The scanner considers evidence from multiple independent categories.
Key telemetry from the analysis
4 signals detected
1 signal detected
Frameworks, platforms, and structured data detected
Technology detection identifies frameworks and platforms used in the implementation. This does not imply the site was AI-generated — many common frameworks are used in both human-made and AI-assisted projects.
Named patterns and framework traces
Builder candidates inferred from framework signatures, headers, and implementation patterns
Public votes on the scanner's classification
Implementation-level observations
| Attribute | Value |
|---|---|
| HTML payload | 0.6 KB |
| DOM structures | 11 |
| Script assets | 2 |
| Stylesheet assets | 1 |
| Token entropy | 1.58 |
| Repeated text | 0.0% |
| Visible words | 3 |
| Headings | 0 |
| Internal links | 0 |
| External links | 0 |
| Forms | 0 |
| Social links | 0 |
| Policy links | 0 |
| Contact signals | 0 |
| Trust signals | 3 |
| Technologies | Vercel v0 Candidate |
The scanner found 4 signals commonly associated with AI/no-code construction with only 1 signal associated with manual engineering and 3 trust markers on the other side. Because the AI/builder signals are dominant, the system classifies this site as likely AI-generated.
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.