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I built a virtual tabletop for playing Dungeons and Dragons

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I built a virtual tabletop for playing Dungeons and Dragons

Diceright is a virtual tabletop for playing dungeons and dragons with friends on the web. You can watch a quick overview of how it works here: https://tinyurl.com/diceright . And there’s a list of the main features right on the homepage. It’s a Ruby on Rails site that makes heavy use of action cable for keeping the maps and tokens in sync for all players. On the front end, I’m using HTML canvas for the maps and a js library called fabric.js for interacting with the canvas. Otherwise, just jQuery on the front end. I optimized it all to work on mobile too. I built this as a side project for fun over of the past couple years. It took a lot longer than expected, but it was also a lot of fun. I did all the design / UX for it too which was a struggle at first but was a great learning experience. Let me know what you think and if you have any questions. Thanks!

Visit productView on Hacker NewsOct 12, 2023
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% 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: tiny, using · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
58%58% 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 · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
32%32% 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
13%13% 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.

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