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A shared emergency number for families

Hacker News

A shared emergency number for families

Hi HN, My wife and I are physicians (YC S17), with young kids and also helping take care of our parents. Very much in the sandwich generation. When I am in the OR or busy and an unknown number comes in, I have no idea. Is this my kid’s school, my dad’s doctor, something urgent, or just spam? I end up being afraid to fully turn on Do Not Disturb because of that. So we built StatPhone - https://statphone.com . It gives you dedicated phone numbers you can share with people you trust. When someone calls one, it rings multiple people at once, and if no one answers, it can automatically escalate to others. No matter what number they are calling from, it shows up as something like “Dad Emergency” or “Kids School” on your phone. For example, we can give one to our kid’s school. It rings both of us at the same time so someone picks up. Or we can give one to my dad’s doctor. It rings me and my sister first, and if we do not pick up, it rolls to my wife and brother in law. It is also useful as a general family emergency line. For kids going to college, for pet sitters, or for things like a doorman calling one number and it ringing everyone in the apartment at once. If no one answers, it uses its own voicemail, not your phone’s, transcribes it, and sends it to everyone. We can still see who actually called via a dashboard or text. We have been using it for school, babysitters, doctors, etc. Figured we would share in case it is useful to anyone else. Thanks, Amit & Puja

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Actual performance

4points
1comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: wife · Missing: supports, reddit linkedin, podcasting
68%68% predicted probability of success on Indie Hackers, 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: ide, io · Missing: https docs, excited, just released
62%62% 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 · Strong signals: answers · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: email, using · Missing: mac, agents, macos
36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
22%22% 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, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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