My
My weekend iOS project "Simple Knot"
Hacker NewsMy weekend iOS project "Simple Knot"
Had some time available and wanted to play with user interface design, so I thought of a simple app that lent itself to that. Coding was roughly 8-10 hours for the whole thing, then another 15-20 hours to take the pictures (homemade studio, iPhone for camera, Gimp to edit). Looking forward to your feedback, and I have promo codes available for anyone that wants them https://itunes.apple.com/us/app/simple-knot/id593421479?mt=8
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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting85%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 · Missing: mobile apps, personal, entrepreneurs56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: apple, user, coding · Missing: mac, agents, macos44%44% 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: io · Missing: https docs, excited, just released41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue18%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, crypto0%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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