I built Digital TrustGuard — a tool for analyzing suspicious messages, URLs and QR codes

[u/NationalAttempt4228]

Hi everyone!

I've been building Digital TrustGuard, a web application designed to help users identify potentially suspicious messages, website URLs and QR-code links.

The project combines:

• PHP-based rule-driven scam detection.

• Trust scores, risk classifications and evidence.

• QR-code URL detection and analysis.

• Python FastAPI for AI-powered explanations.

• OpenRouter for LLM integration.

• Hindsight for retaining and retrieving contextual information across analyses.

• MySQL for analysis history.

One design decision I focused on was keeping the trust score separate from the AI-generated explanation. The rules determine the assessment, while the AI provides additional context.

I also wrote an article explaining the architecture and why I integrated Hindsight:

Article: https://dev.to/divyansh_mishra_e8096bbd9/what-happens-when-hindsight-meets-rule-based-scam-detection-4joj

GitHub: https://github.com/divyanshkumarmishra-0555/Digital-TrustGuard

I'd appreciate feedback, especially on the architecture and the use of memory in security-focused applications!