Dicetray.gg A virtual dice tray for tabletop RPGs and board games

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I have been working on dicetray.gg for a while now, and I am looking to show it off and get some feedback. Doing my best to emulate a physical dice tray. Right now, you can: -Add up to 10 of each dice to your tray. -Roll all dice in your tray, or select just some to roll by clicking on them. -Join a multiplayer log to see and share roll results with friends. Features I am looking to add soon: -Per dice customization. You can now use the (`) key to open a development panel to customize all the dice. I would appreciate any feedback on the UI, user experience, and performance as well as any feature ideas.

Visit product View on Hacker NewsAug 15, 2023

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64%64% 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: user, physical, open · Missing: mac, agents, macos

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% 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

44%44% 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 · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

12%12% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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