Op

Open-source Japanese learning Platform inspired by Monkeytype

Hacker News

Open-source Japanese learning Platform inspired by Monkeytype

As a long time Japanese learner, I've always wanted there to be a simple, free online trainer for learning and grinding Japanese kanji and vocabulary - like Monkeytype in the typing community (fun fact: we actually have 2 Monkeytype devs on our team now!). I'm doing this because I grew tired of all the subscriptions and paywalls, which are unfortunately very common in the Japanese learning apps market. That's why I want to make the most fun, user-friendly and customizable platform for learning Japanese currently available, and the first one that is (and will be, forever) completely free, ad-free and open-source - driven not by profit, but made by the community, for the community. We already managed to get around 600 stars on GitHub and about 7k monthly users for the app. That being said, I recognize that the app is still in its very early alpha stages and is, objectively speaking, behind paid Japanese learning solutions (at least for now) - which is why I want to change that and make it the first entirely free and open-source platform for learning Japanese to provide a real, high-quality alternative to all the current paid Japanese learning resources, which I believe we can achieve with the open-source community's help. Why am I doing this? Because weebs deserve to have a free, open-source learning platform too (I'm one of them, don't judge...) どうもありがとうございます!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%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: apps, user, open · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
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.
AppSumoStrong fit for a featured deal · Strong signals: platform, friendly, users · Missing: plus, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, month, monthly · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: profit, subscription · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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