Actual performance
246upvotes
9comments
Made the leaderboard
Traction signals
Makers1
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: google · Missing: mac, agents, macos77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto53%53% predicted probability of success on BetaList, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive40%40% 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, revenue16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting15%15% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Correct prediction on native model
Similar products
Di
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 News56Calcolo Stipendio Netto38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Calcolo Stipendio Netto, Dal Lordo al Netto in Pochi Secondi
Indie Hackerscommitment-side-project
wherEvents51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
¡descubre los mejores eventos cerca de ti al instante!
Product Hunt+6Mentio55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Turn Conversations into Leads with Al Agents
Indie Hackers1aiCa
Campistry – Camping on Demand49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Campistry – Camping on Demand
Hacker News6Lu
Lugg – Your on-demand mover49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Lugg – Your on-demand mover
Hacker News3