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Apple TV Quiz App (+ Promo Codes)

Hacker News

Apple TV Quiz App (+ Promo Codes)

Anyone who has a new Apple TV - I've built a simple trivia/quiz app and am looking to get some feedback so I can figure out what needs improving. I built it in my spare time and tried to cut it down to the most simple idea that was still really fun but I'd like to get some more opinions from actual users. I'm still not 100% happy with the controls on the remote so if anyone has any suggestions for how to improve it I'm more than happy to have a go at implementing it Unfortunately it's quite hard to get feedback at the moment as no-one seems to be able to find apps on the Apple TV (you have to search for everything!) so it's hard to know what to implement next I've added some promo codes here (now I figured out how to do it) so you don't have to spend 79p too :) Search for: 60 Second Quiz (although I think you currently have to redeem the codes on an iOS device or iTunes) Promo codes: Y69T7MWH6APE 3WLAFHW6PW3Y RFLNK77A4AX3 Y7XTAKJ3EKLJ A4P4J9LFK7PH

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1points
2comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
90%90% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: apple, apps, user · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, users · Missing: mobile apps, personal, entrepreneurs
44%44% 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, intuitive
42%42% predicted probability of success on AppSumo, 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
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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.

Correct prediction on native model

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