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Open-source Solitaire games built with Flutter and a card game engine

Hacker News

Open-source Solitaire games built with Flutter and a card game engine

I recently launched Cards, a collection of Solitaire-style card games built with Flutter. You can play a hosted version on the web at https://cards.jlogical.com/ Games included Klondike (Classic Solitaire), Golf Solitaire, and Free Cell. Each game has three unlockable difficulties. Players can unlock achievements, customize card backs and backgrounds, and track fastest times for each mode. It’s fully responsive (works across phone, tablet, and desktop) and built using a Flutter package I developed called card_game, which handles common card interactions like dragging, flipping, and layout management. I recently released card_game v2.0.0 with a lot of improvements. Cards is the first large-scale project using the new version. You can find card_game on pub.dev here: https://pub.dev/packages/card_game The Cards project is fully open source: https://github.com/JLogical-Apps/cards Happy to answer any questions about the architecture if people are curious!

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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 · Missing: supports, reddit linkedin, podcasting
78%78% 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: apps, new, using · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, game engine, 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.
TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
50%50% 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
15%15% 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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