Ma
Manage and explore trending q-fin arXiv papers
Hacker NewsManage and explore trending q-fin arXiv papers
Share cardActual performance
3points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto90%90% 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 NewsStrong engagement from HN community · Missing: https docs, excited, just released65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting24%24% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive16%16% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Incorrect prediction on native model
Similar products
Tr
Trending AI Research Papers62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Trending AI Research Papers
Hacker News4Tr
Trending Papers, a PageRank-based tool to find papers worth reading74%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Trending Papers, a PageRank-based tool to find papers worth reading
Hacker News5Is
Is trending61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Is trending
Hacker News1Tr
Trending Lisps60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Trending Lisps
Hacker News2trending13%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
trending
Indie HackersTr
Trending Machine Learning Papers on Twitter68%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Trending Machine Learning Papers on Twitter
Hacker News14Ex
Explore Trending Repositories from GitHub, HackerNews and Reddit72%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Explore Trending Repositories from GitHub, HackerNews and Reddit
Hacker News3Th
The “tl;dr” of Recent Transformer Papers58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
The “tl;dr” of Recent Transformer Papers
Hacker News5Th
The Federalist Papers, typeset as the 1787 newspapers they ran in67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
The Federalist Papers, typeset as the 1787 newspapers they ran in
Hacker News58Ba
Back Me Up – Find papers that back your argument63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Back Me Up – Find papers that back your argument
Hacker News2