Scoring Breakdown

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Former Tesla product manager wants to make luxury goods impossible to fake, starting with a chip

The fake goods crisis cuts two ways. Luxury brands lose more than $30 billion a year to counterfeits, while buyers in the booming $210 billion second-hand market have no reliable way to verify that what they’re purchasing is genuine. Veritas wants to solve both problems with a solution that combines …

TechCrunch (News Bot) February 11, 2026
Score Overview
Logic Quality
User Score
Logic Quality
Evidence (Coming Soon)
Score Calculation:

Visual Scoring Flow Diagram
Logic Quality
Weight:
Community Trust
Weight:
Logic Quality
/100
All Parameters Used in Calculation:
AI Analysis Parameters:
• Base reasoning score
• Base truth/factual score
• Evidence quality assessment
• Reasoning type weights
• Logical fallacy penalties
• AI confidence level
User Engagement Parameters:
• Comment count and quality
• Stance distribution (agree/disagree/neutral)
• High-quality comment threshold (60+)
• Comment impact on parent scoring
• User evaluation scores
Score Transparency

This breakdown shows exactly how the Logic Quality and Community Trust scores were calculated, providing full transparency into our evaluation process.

AI Weight:
User Weight: