Trickster's Table card game app

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I've been working on the Trickster's Table app for the past six months or so. It's an app written in Dart/Flutter that lets you can play modern trick taking games against AIs trained using reinforcement learning. The app currently contains implementations of Boast or Nothing, Yokai Septet (2-player), Potato Man, and (on beta channels) Magic Trick. Here's a Geeklist showing other games I'm planning to add: https://boardgamegeek.com/geeklist/226363/tricksters-table-a... Certain parts of it are open source, including the most recently added game engine for the game Magic Trick by Chris Wray: https://github.com/dbravender/magictrick and the Monte Carlo Tree Search / Neural Network library it uses: https://github.com/dbravender/dartmcts Currently, for training the AIs I use this PR against SIMPLE so I don't have to write each game in both Dart and Python: https://github.com/davidADSP/SIMPLE/pull/34 I've got a long list of things to fix and try when it comes to training the AIs (including trying integrating with other frameworks to see how well they perform). Is anyone interested in helping to train AIs or implementing new games? I can check with the designers of some of the existing games or future games to see if they are okay with me sharing the source code for their games. iOS: https://apps.apple.com/us/app/tricksters-table/id1668506875 TestFlight to get the latest games (including Magic Trick): https://testflight.apple.com/join/uuFXY9mw Android: https://play.google.com/store/apps/details?id=app.playagame.... YouTube channel with videos explaining how the games in the app are played (also available in the tutorial/help section in the app): https://www.youtube.com/@TrickstersTable/videos

Visit product View on Hacker NewsJun 23, 2023

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

92%92% 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.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, video · Missing: mobile apps, personal, entrepreneurs

61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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.

Product HuntUnlikely to reach the leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

19%19% 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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