I

I made an iOS app blocker that helps you learn new words

Hacker News

I made an iOS app blocker that helps you learn new words

Hello HN! I created Gobble, an iOS app designed to block distracting apps until you complete your flashcard reviews. I’ve been learning languages on and off for the last 10 years, and one of my favorite activities is reviewing flashcards. I know this can be a huge drain, but it is a highly efficient way to learn new words [0]. Flashcards aren't a silver bullet, but I find that I can review hundreds of them if I break the sessions into shorter intervals throughout the day. The problem with reviewing flashcards on my phone, though, is that while it's an amazing tool, it’s also incredibly easy to get distracted by other apps. Right now, the app includes the full N5 Japanese vocab list, as well as basic content for French and Spanish, with more content on the way. I'd love to hear your feedback! [0] https://en.wikipedia.org/wiki/Spaced_repetition

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, ios · Missing: supports, reddit linkedin, podcasting
89%89% 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, way · Missing: mobile apps, personal, entrepreneurs
74%74% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, efficient · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
42%42% 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, revenue
22%22% 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
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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