My

My random trick generator apps (skateboarding, snowboarding, skiing)

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My random trick generator apps (skateboarding, snowboarding, skiing)

I just finished porting my suite of random trick generating apps from iOS to Android. I've got three of the major action sports covered. The apps give you a random "extreme" thing to try when you're in the skate park or terrain park They also come up with combos of tricks that make sense for the different features you're doing them on. So you can tell the app you're going to hit a rail, then a jump, then another rail, and it will put the right sequence of tricks together (accounting for landing with the other foot forward or even landing backwards like in skiing). They also have difficulty settings so you can learn about some of the most challenging tricks in the different sports. Snowboarding has a "Pro" mode where you're literally rolling tricks they do in the Olympics. Anyway, here are the links to the products on Google Play Skate: https://play.google.com/store/apps/details?id=com.senditapps... Ski: https://play.google.com/store/apps/details?id=com.senditapps... Snowboarding: https://play.google.com/store/apps/details?id=com.senditapps...

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
73%73% 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.
TrustMRRFits verified-revenue profile · Strong signals: ios, apps, google · Missing: mobile apps, personal, entrepreneurs
68%68% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, 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: google, apps · 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
23%23% 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.

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