I
I built a news app with topic grouping model
Hacker NewsI built a news app with topic grouping model
Share cardActual performance
3points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: model, new · Missing: mac, agents, macos69%69% predicted probability of success on Product Hunt, 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 released49%49% 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, personal36%36% predicted probability of success on TrustMRR, 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive28%28% 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, revenue21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto15%15% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
Similar products
Ne
News parsing API with a built-in topic prediction58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
News parsing API with a built-in topic prediction
Hacker News3To
Topic Story – daily topic modeling of the news31%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Topic Story – daily topic modeling of the news
Hacker News1Di
DidSayThat – What has politician X said about topic Y?54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
DidSayThat – What has politician X said about topic Y?
Hacker News8A
A PureScript Implementation of LDA Topic Modeling42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
A PureScript Implementation of LDA Topic Modeling
Hacker News4To
Topic Modeling with Bert44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Topic Modeling with Bert
Hacker News2BE
BERTopic – Topic Modeling with Bert44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
BERTopic – Topic Modeling with Bert
Hacker News2To
Topic52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Topic
Hacker News56I
I built a bot to remind me of what topic I should revise next55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
I built a bot to remind me of what topic I should revise next
Hacker News1A
A News App for Geeks35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
A News App for Geeks
Hacker News6Nu
Nuzi News App35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Nuzi News App
Hacker News1