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I spent 2 years building an iOS app no one asked for

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I spent 2 years building an iOS app no one asked for

i built a heart rate zone training app for runners. ios + apple watch. custom zones, real-time haptics, post-run graphs. all the “smart” stuff you’d expect. but i never talked to a single runner before building it. not even myself lol. started as a thesis → kept building → added features → added a paywall (€4/month!) → launched it → and yeah… no one cared. zero feedback. zero users. just code in the void. i recently made the whole thing free. not trying to “sell” anything now — just seeing if it’s actually useful, and listening this time. curious how others handle this. ever build something 100% wrong?

Visit productView on Hacker NewsMay 31, 2025
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Indie HackersFits the IH revenue-focused audience · Strong signals: started, ios · Missing: supports, reddit linkedin, podcasting
91%91% 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: apple, user, single · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, month, users · Missing: mobile apps, personal, entrepreneurs
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
51%51% 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 · Strong signals: training · Missing: arr, mrr, revenue
40%40% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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