Round 1 Winners!
Proof of Usefulness Report

Chalearn

Analysis completed on 3/19/2026

+222.06
Proof of Usefulness Score
Gaining Momentum

Chalearn demonstrates strong technical relevance by organizing computer vision challenges (e.g., CVPR, LAP). However, the submission itself features exaggerated claims ('most people have used my product', 'everyone') and vague financial metrics ('all time marketcap: 2500000'). This discrepancy between the actual academic project's real-world utility and the poorly substantiated submission data heavily penalizes evidence of traction, reach, and response quality, resulting in a moderate score.

Ready to Compete for $150k+ in Prizes?

Move this data into a HackerNoon blog draft to become eligible for your share of $150k+ in cash and software prizes

View All Reports

Score Breakdown

Real World Utility+112.50
Audience Reach Impact+15.00
Technical Innovation+75.00
Evidence Of Traction+6.25
Market Timing Relevance+50.00
Functional Completeness+2.50
Subtotal+261.25
Usefulness Multiplierx0.85
Final Score+222

Project Details

Project URL
Description
LAP : Looking at People . A challenge series pushing the state-of-the art in computer vision to detect, recognize, and interact with humans . Current work: Face Anti-Spoofing challenge , CVPR 2024 ....

Algorithm Insights

Market Position
Growing utility with room for optimization
User Engagement
Documented reach suggests active user community
Technical Stack
Modern tech stack aligned with sponsor technologies

Recommendations to Increase Usefulness Score

Document User Growth

Provide specific metrics on user acquisition and retention rates

Showcase Revenue Model

Detail sustainable monetization strategy and current revenue streams

Expand Evidence Base

Include testimonials, case studies, and third-party validation

Technical Roadmap

Share development milestones and feature completion timeline