I spent 6 years making a cool free game

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Hey everyone! Long time lurker, first time poster. I've just reached beta in my game BoxByte Lyte. -What is it- It's a free 3d arcade game for hardcore players that uses color matching in interesting ways. Also it's a one button game, well movement and one button. Cool huh? The final release will be free too. -What I need- This is a very challenging game, so I really need to balance the difficulty so that it's still hard but not impossible. It would be great just to hear your experiences of the game and how far in it you got. -Where can I report bugs?- https://bitbucket.org/ZayWolfe/boxbyte-lyte/issues -So where's the game?- Windows: https://www.dropbox.com/s/3143ynz2hlqhg58/BoxByteLyte%2012.17.15-win.zip?dl=0 Mac: https://www.dropbox.com/s/welj1gswunfm4tq/BoxByteLyte%2012.17.15-mac.zip?dl=0 Linux: https://www.dropbox.com/s/ypvtnndmzklj17e/BoxByteLyte%2012.17.15-linux.zip?dl=0 Win VR: https://www.dropbox.com/s/bm0ydtzra4sjml0/BoxByteLyte%2012.17.15-winVR.zip?dl=0 (Warning VR isn't going to be an official feature. It's just a cool experiment for kicks. Might be nausea inducing. I just think it's cool :3) For the most accurate movement, I recommend playing with a gamepad of some kind. So if hard challenging gameplay is your thing, why don't you see if you have what it takes to beat my game... Mwahahaha! 13 gates, can you reach the end? Cheers!

Visit product View on Hacker NewsDec 18, 2015

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best fitHighest predicted score across all platforms for this description.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

25%25% 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

14%14% 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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