Super retro Windows 95 Solitaire for iOS

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This might be something y'all find amusing. My partner and i created a Solitaire clone for iOS, in a weird trippy mishmash of Windows 95 and Windows 3.11 visuals, including a lot of artistic licence for easter eggs. Everything is lovingly pixellated, and the end game animation, well, we find it quite satisfying. Featuring all the original card backs you know and love, no ads, no analytics, no nasties, and artisanally extracted bitmaps from the original cards.dll! It's perpetually free of charge to play. https://apps.apple.com/us/app/sol-exe-retro-solitaire/id1542164345#?platform=iphone

Visit product View on Hacker NewsDec 4, 2020

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

84%84% 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 · Missing: mobile apps, personal, entrepreneurs

66%66% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io, including · 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.

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

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% 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

18%18% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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