Learn Japanese Kanji, no silly mnemonics

Hacker News

Hello all, I've developed a resource for learning Japanese Kanji, for which I intend to launch a Kickstarter campaign in the upcoming months. I've just recently published a website which explains and introduces the project, including the in-depth guide for using the cards to learn Japanese. Interested parties can subscribe to the mailing list to which I will post the launch announcement. I'm happy to answer any questions and discuss feedback regarding the technical or otherwise content of the website (e.g. comments from a marketing standpoint are also welcome). Cheers, Rodrigo

Visit product View on Hacker NewsDec 24, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, plain · Missing: mac, agents, macos

85%85% 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: including · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released

43%43% 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, intuitive

33%33% 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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