Ge
Geospatial Data → Graphs (Networks)
Hacker NewsGeospatial Data → Graphs (Networks)
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto83%83% predicted probability of success on BetaList, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting32%32% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
Similar products
Ne
Network Dynamics Open Dataset – 2.5M Graphs and 38000 Networks (1TB)54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Network Dynamics Open Dataset – 2.5M Graphs and 38000 Networks (1TB)
Hacker News10Gr
Graph Convolutional Networks – Intro to neural networks on graphs55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Graph Convolutional Networks – Intro to neural networks on graphs
Hacker News106In
Introduction to Recurrent Networks in TensorFlow66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Introduction to Recurrent Networks in TensorFlow
Hacker News6Re
Recurrent Entity Networks with TensorFlow65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Recurrent Entity Networks with TensorFlow
Hacker News37Eq
Equilibrium in Cryptoeconomic Networks51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Equilibrium in Cryptoeconomic Networks
Hacker News54Lo
Lorentz Embeddings of Graphs in 2-5 Dimensions55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Lorentz Embeddings of Graphs in 2-5 Dimensions
Hacker News2La
Lazily-evaluated graphs from sympy expressions48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Lazily-evaluated graphs from sympy expressions
Hacker News5Si
Sierpiński and Other Kronecker Graphs with the GraphBLAS39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Sierpiński and Other Kronecker Graphs with the GraphBLAS
Hacker News2Se
Semi-Supervised-Segmentation-on-Graphs31%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Semi-Supervised-Segmentation-on-Graphs
Hacker News2E-
E-graphs and equality saturation in Haskell66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
E-graphs and equality saturation in Haskell
Hacker News22