ImAlive – Personify your phone

Hacker News

Hey there :) My name is Hay and i'm the owner of ImAlive. ImAlive is an Android mobile app that allow users to change the way their phone behaves by applying a character into their phone! A Character is a unique skin applied to the phone, it contains image and sound assets, along with a Javascript that controls the way that the character behaves. Users are also able to choose any character they want based on the things they like from a variety of categories! and they can also create their own character! I have made guides and tutorials to help my users, and opened a forum to answer any questions they might have :) Website: https://www.imaliveweb.com Promo video link: https://www.youtube.com/watch?v=5jXX2M0c_2M Beta registration link: https://play.google.com/apps/testing/hayzohar.imalive The app is on a mid-level Beta stage, and im looking for more beta testers to help me out. I'd be glad to hear and implement any ideas so please feel free to send them to me :) Thanks!!

Visit product View on Hacker NewsOct 10, 2017

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

84%84% 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: google, apps, user · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, 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

48%48% 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: apps, video, google · Missing: mobile apps, ios, personal

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% 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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