AE
AED Map – Mobile App for Automated External Defibrillators
Hacker NewsAED Map – Mobile App for Automated External Defibrillators
I've created mobile app in Flutter that allows people to look for nearest AED (automated external defibrillator). In case of sudden cardiac arrest, usage of AED increases chances of survival from 5% to 70%. Data comes from openstreetmap database. My app also allows users to navigate via pedestrian routing engine. Users can also contribute to OSM database.
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3points
Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: user, open · 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 NewsStrong engagement from HN community · Missing: https docs, excited, just released55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · 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 · Strong signals: users · Missing: plus, platform, intuitive34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto18%18% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Incorrect prediction on native model
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