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Linkfy – On-Device URL Cleaner for iOS
Hacker NewsLinkfy – On-Device URL Cleaner for iOS
I built Linkfy, an iOS app that strips tracking junk (utm_*, fbclid, etc.) from URLs. Runs entirely on-device, no server Works via share sheet or inside the app Keeps essential parts of the link intact I made it because I was tired of sharing messy, tracking-filled links. App Store: https://apps.apple.com/us/app/ai-link-cleaner-linkfy/id67497... Code (Swift package): https://github.com/zeynep-yildiz/Linkify Happy to hear feedback and edge cases!
Share cardActual performance
3points
4comments
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, podcasting75%75% 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, entrepreneurs57%57% 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, apps, code · Missing: mac, agents, macos52%52% 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 released36%36% 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, intuitive32%32% 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, revenue24%24% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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