Proof of Usefulness Report

Preset

Analysis completed on 1/28/2026

+404
Proof of Usefulness Score
Certified Problem Solver

Preset is a legitimate, venture-backed Series B company (approx. $48M funding, $5M+ revenue) offering a managed version of Apache Superset. While the project's real-world utility and technical foundation are exceptional, the submission itself was of very low quality. The submitter ('MysticSpirit') provided exaggerated claims ('most people have used my product') and lacked concrete evidence, resulting in severe penalties to the Quality Factors for Traction and Reach. The final score (404) places it above the HackerNoon baseline (333) due to its larger verifiable scale, but significantly below its potential (600+) due to the poor quality of the proof provided.

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Score Breakdown

Real World Utility+23.75
Audience Reach Impact+8.00
Technical Innovation+13.50
Evidence Of Traction+11.25
Market Timing Relevance+9.00
Functional Completeness+0.50
Subtotal66.0 (Raw Weighted Score)
Usefulness Multiplierx1.02
Final ScoreNaN

Project Details

Project URL
Description
Preset is fully managed Apache Superset, the open-source project that started life as a hackathon project at Airbnb in the summer of 2015. The goal was to build an open-source application to enable users to slice, dice, and visualize data out of Apache Druid, an up-and-coming, blazing-fast, real-time, distributed in-memory database. Superset grew quickly, taking on more and more use cases, eventually surpassing Tableau as Airbnb’s main data visualization solution. Superset was established as a full-fledged open-source project, incubating with the Apache Software Foundation, in 2016. Today Superset is the leading open-source analytics platform, with one of the fastest-growing communities on GitHub and enterprise users at data-hungry companies like Airbnb, Lyft, and Twitter.\n\n\n\n

Algorithm Insights

Market Position
Strong market validation with clear user adoption patterns
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