Ae
Aecius – Website Recommendations based on browsing history
Hacker NewsAecius – Website Recommendations based on browsing history
Share cardActual performance
1points
1comments
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos68%68% 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 NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto29%29% predicted probability of success on BetaList, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting20%20% 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, revenue18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Incorrect prediction on native model
Similar products
Ga
Garbage Collector for Browsing History50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Garbage Collector for Browsing History
Hacker News2Ch
Churchhill – Browsing History Garbage Collector50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Churchhill – Browsing History Garbage Collector
Hacker News1iO
iOS Product Suggestions Based on Browsing History39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
iOS Product Suggestions Based on Browsing History
Hacker News10Sq
Squash – your browsing history as context for ChatGPT26%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Squash – your browsing history as context for ChatGPT
Hacker News2A
A Tree-Based Way to Navigate Browsing History52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
A Tree-Based Way to Navigate Browsing History
Hacker News43Wo
WorldBrain – full text, local search of your browsing history48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
WorldBrain – full text, local search of your browsing history
Hacker News229De
Dezip – a website for browsing source code archives55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Dezip – a website for browsing source code archives
Hacker News2Ex
Extension for searching and exploring your full browsing history35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Extension for searching and exploring your full browsing history
Hacker News1Qu
Quora for Recommendations43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Quora for Recommendations
Hacker News1Ti
Tired of Netflix recommendations? Televisor57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Tired of Netflix recommendations? Televisor
Hacker News1