PhraseExpander
Indie HackersText expansion / template generation for Medical Doctors
I initially started the software as I wanted to a way to reply to emails most effectively. So it was a generic tool initially. Then we discovered that medical doctors were among our most active users and decided to pivot
Visit productView on Indie HackersAug 1, 2006commitment-side-projectemployees-under-10founders-code-yes
Share cardActual performance
Did not reach leaderboard
Launch Intel predictions
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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: started · Missing: supports, reddit linkedin, podcasting31%31% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, email · Missing: mac, agents, macos26%26% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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