The full source code and assets for my custom game engine and game

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I always enjoy reading posts about indie game development and looking at the source code of games other people have built. So for any game I release I naturally I want to do the same. https://github.com/Mknsri/HockeySlam Hockey Slam is a hockey shootout game on Android which I built almost from scratch. Some of the engine features include 3D graphics with PBR materials, a rudimentary physics system, replays, a multithreaded job system and a hot-reload DLL platform for developing on Windows. I detailed the creation of the game in it's own post, but now that time has passed I am releasing the source code and assets under the MIT license. You can still find the making of here: https://hockeyslam.com/makingof I'm happy to answer any questions you might have.

Visit product View on Hacker NewsJul 10, 2023

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

81%81% 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.

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

63%63% 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 · Strong signals: platform · Missing: plus, intuitive, reviews

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% 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

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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