Manabi Reader – Learn Japanese by Reading on iOS and macOS (SwiftUI)

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I quit my job last year and worked mostly on this full SwiftUI rewrite of my Japanese reading app, Manabi Reader. The rewrite gave me the opportunity to expand to macOS (and without Catalyst so it feels extra native) and redo the data layer to be offline-first via Realm with iCloud sync. The biggest differentiators compared with other language study apps are that it tracks every word/kanji you read so that you can see how much of a given webpage/article you're already familiar with and other features/analytics built on that foundation; it automatically builds a personal corpus of example sentences; and that it does all the Japanese tokenization/dictionary lookups locally on-device and in a flexible web browser-like UI with readability mode, to be respectful of your privacy and to work offline. I've also added Anki integration. Tap a word, tap another button to save it to Anki with the original source material sentence and URL. I have a Manabi Flashcards app as well if you don't like Anki. Packed with free features. See what percent of each article's vocabulary you're familiar with based on your reading history. Scan paragraphs of text with your camera to look up words. Japanese/English dict. Native Japanese web dicts. Look up kanji by drawing. Expanded JLPT levels. RSS. Web browser UI. Save links from other apps. Works offline. Readability mode. Tap words to look them up. Furigana depending on your familiarity with each word. Future plans: besides more features (ePUB, YouTube, mpv player, WaniKani integration, more languages, etc), I’m also preparing the underlying SwiftUI web browser lib as open source and will launch it as a WebKit-based browser/reader option, which I’m excited to get out alongside other interesting recent entrants to the desktop and mobile browser market.

Visit product View on Hacker NewsJul 10, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apps · Missing: agents, agent, cursor

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: para, ios · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, ide · Missing: https docs, just released, exist

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: ios, personal, apps · Missing: mobile apps, entrepreneurs, video

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

29%29% 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, revenue

18%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, 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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