A New Game I Built in Two Months

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Sorry for using a brand new account. I've lost access to my old account of several years. This is a game that I spent the last two months working on: https://play.google.com/store/apps/details?id=com.noatechnologies.android.shooter I built it completely from scratch. That means that I built the graphical user interface engine, the collision engine, rendering engine, and physics engine starting from zero. I release it to the Play Store a couple of days ago but so far I haven't gotten a single download. I'm starving for some feedback. I think it is a decent game for spending the time away in the bus, subway, or the doctor's office but maybe that is just me. I need honest feedback. I want to know if it is total crap or whether I'm in the right direction. Please help me. A bit about me. I'm located in the northeast, Connecticut and sometimes NYC. I've been trying to teach myself everything about games since 2010. Mainly I've been trying to work on this game: https://play.google.com/store/apps/details?id=com.noatechnologies.android.flyingfighterbeta But it feels like the Never Ending Story. I'm taking an indefinite break from that and working on creating the smallest games that I can possibly make. Like the game I just released. I also worked on this game a couple of years ago with no success if anyone is curious: https://play.google.com/store/apps/details?id=com.noatechnologies.android.machinabuilderlite

Visit product View on Hacker NewsDec 24, 2015

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

86%86% 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: mac, google, apps · Missing: agents, macos, agent

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: just released, io · Missing: https docs, excited, exist

57%57% 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, month, google · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface, builder · Missing: plus, platform, intuitive

41%41% 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

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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