Teddy Bear Tracker iOS App

Hacker News

Two weeks ago when walking around my neighborhood I noticed a strange amount of teddy bears placed in the windows of homes. When I got home I searched the internet and found https://www.nytimes.com/2020/04/03/style/teddy-bear-scavenge... describing that this was being done to provide additional entertainment for people going on walks during these times of social distancing. This past week I decided to repurpose some old code into an app that would allow me to keep track of the teddy bears I found while on my own walks. It's quite simple but I hope others can get some enjoyment out of it! :) Here is the Apple App Store link: https://apps.apple.com/us/app/teddy-tracker/id1507523019

Visit product View on Hacker NewsApr 19, 2020

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18points

5comments

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TrustMRRFits verified-revenue profile · Strong signals: ios, apps · Missing: mobile apps, personal, entrepreneurs

67%67% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

42%42% 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, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apple, apps, code · Missing: mac, agents, macos

30%30% 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

24%24% 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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