Ti

Tile Realm board game app

Hacker News

Tile Realm board game app

Android - https://play.google.com/store/apps/details?id=net.bravender.tilerealm iOS - https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewSoftware?id=1425466484&mt=8 I originally implemented this in Python a year ago but when I wanted to make it into an app I found it very difficult to get Python running on Android so I rewrote it in Kotlin/React. I found Kotlin to be a breath of fresh air. Check out this video if you are interested: https://www.youtube.com/watch?v=FDOECr-sT6U. For me refactoring in Kotlin was the easiest I've ever had it in my life and I was surprised by how few transpiler issues I had. I've used other transpiled languages and I'm used to getting into situations where there are very difficult-to-track-down bugs but I only had a few issues related to null in Javascript while working on this (Kotlin assumed a value could not be null but it was in some instances). Here are some statistics: Android app - 146 lines of Kotlin (basically just an embedded webview) iOS app - 226 lines of Swift (basically just an embedded webview) Game and AI: Total - 1,628 lines of Kotlin MCTS library - 195 lines of Kotlin Game logic - 452 lines of Kotlin Rules page - 109 lines of React/Kotlin Game react components - 288 lines of React/Kotlin Game logic tests - 183 lines of Kotlin App logic - 273 lines of React/Kotlin ... etc I wrote the AI for this using a modified form of MCTS (since full-on MCTS on Javascript would melt phones). Please let me know what you think! I know it needs to scale better (it must look horrible on iPads) but I'm horrible at UIs.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
85%85% 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
64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, 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
35%35% 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
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
25%25% 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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