My
My Sunday Morning Project Encrypts Plaintext On Your iPhone
Hacker NewsMy Sunday Morning Project Encrypts Plaintext On Your iPhone
https://itunes.apple.com/us/app/text-locker/id706492969 This is my first iOS app. It only took me few hours to complete (and about an hour the next Sunday to fix 2 bugs). My participation in some cryptography-related research this summer inspired me. After investigating options for ciphering text on iOS, I thought this App needs to exist. The algorithm used is AES128+CBC mode followed by base64.
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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting84%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.
Product HuntOn track for Day 1 leaderboard · Strong signals: apple, plain · Missing: mac, agents, macos67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs65%65% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, io · Missing: https docs, excited, just released47%47% 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, intuitive40%40% 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, revenue23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency0%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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