Novelty Score
Training data platform for autonomous vehicles. Computer vision annotation at scale for self-driving cars. Acquired by Uber ATG in 2019.
21
Overall Novelty
Weighted score: how differentiated is this product's research?
40
Uniqueness
4 other products use the same papers on avg
18
Research Recency
Are the underlying papers recent (cutting-edge) or old (commoditized)?
0
Founder Authorship
Built on external research — execution-dependent
How to read this report
Novelty Score (0–100)
Measures how differentiated this product's technical approach is. Combines three signals: Uniqueness (40%) — fewer products on the same papers means a more unique approach. Research Recency (30%) — building on recent papers (2020+) suggests cutting-edge work; older papers (pre-2015) are more commoditized. Founder Authorship (30%) — if the founder authored the underlying papers, they have deep domain expertise and a technical moat.
Research Lineage
The academic papers this product builds on. Each link has a source type (who declared it: the maintainer, automated extraction from READMEs, community contribution, or AI detection) and a confidence score (0–100%). Higher confidence = stronger evidence.
Competitive Map
Other products that build on the same research papers. The overlap % shows what fraction of this product's papers are shared. 100% overlap = building on identical research. 10% = mostly different foundations.
Domain Trends
Are the domains this product operates in accelerating (more products being built recently), steady, or slowing? Based on the rate of new paper-to-product links over the last 30 and 90 days.
Paper Adoption Timeline
Shows when each product adopted each paper. If many products adopted the same paper recently, it's a trending technique. If only this product uses it, it's a differentiated bet.
Research Lineage (2 papers)
The academic papers this product builds on, with provenance
Competitive Map (8 products on same research)
Other products building on the same papers — higher overlap = more similar technical approach
timm (PyTorch Image Models)
by Ross Wightman · Vancouver
50%
1 shared papers
Covariant Brain
by Peter Chen · Berkeley
50%
1 shared papers
Lunit INSIGHT
by Hyun Kim · Boston
50%
1 shared papers
Allen AI (AI2)
by Ali Farhadi · Seattle
50%
1 shared papers
Xnor.ai (acquired by Apple)
by Rory Finnegan · Seattle
50%
1 shared papers
Machine Learning Mastery
by Jason Brownlee · Sydney
50%
1 shared papers
Scale AI Data Platform
by Alexandr Wang · San Francisco
50%
1 shared papers
openpilot
by George Hotz · San Diego
50%
1 shared papers
Domain Trends
Is this product's domain accelerating or cooling down? Based on new paper→product links over time
Paper Adoption Timeline
When did each product adopt each paper? Clustering = trending technique. Solo adoption = differentiated bet
You Only Look Once: Unified, Real-Time Object Detection
3 products built on this paper
ImageNet: A Large-Scale Hierarchical Image Database
7 products built on this paper
About this report
Research lineage is based on builder-declared paper links with provenance tracking. Novelty scores are computed from paper uniqueness (fewer products = more novel), research recency, and founder authorship. Competitive maps show other products building on the same research papers. This is not investment advice.