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Mandarin Melon – A webapp for learning Chinese by reading social media

Hacker News

Mandarin Melon – A webapp for learning Chinese by reading social media

Hi HN, I built Mandarin Melon (mandarin-melon.com) to scratch an itch as a Chinese learner. I've found with language learning that I learn best when I'm getting a lot of high quality comprehensible input. Reading, listening, watching videos. But since my Chinese isn't great, it's kinda hard to find content that is at my level, and is actually engaging. A lot of people give advice like "watch Peppa Pig in Chinese", but IMO, Peppa Pig is not particularly beginner friendly and is also really boring. For reading, similarly, graded readers can target a specific reading level and are really useful, but get boring fast. On the other hand, social media is about the ultimate form of engaging content. But as an intermediate learner, I quickly get lost trying to use actual Chinese social media platforms, scrolling without really understanding enough to be learning. So I built Mandarin Melon as a way to read social media posts that use only the characters you've already studied. (1.) Textbook vocabulary tailored feeds - If you study with the standard HSK textbooks, this creates a tailored feed using just characters you already know. For example, if you're at HSK level 3 here is a collection of 56,000+ posts that only use characters from HSK 3 and below: ( https://mandarin-melon.com/bylist/hsk-old?level=3&atLevel=2&... ). You can also choose to introduce posts with 1-3 characters you don't know, to push your learning and expand the base of posts to browse. (2.) A mode for new learners ( https://mandarin-melon.com/learn/onebyone/0 ) - In this mode, characters are introduced one-by-one, with definitions / pronunciations for the new character. The characters you learn are ordered such that each new character maximizes the number of new posts you can read by learning it. This wouldn't be a good way to learn Chinese on its own, but would be fun for a new learner to dip their toes into Chinese social media. Personally I'm using the app most days. I find the bite-sized bits of content / learning a really motivating way to keep up my daily Chinese practice. I also find the little stories of peoples posts really fun and natural in a way that textbooks / graded readers are not. If there are any Chinese language learners, or folks interested in Chinese social media, I'd love to hear thoughts / feedback. Thanks for checking it out!

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: maximize · Missing: supports, reddit linkedin, podcasting
95%95% 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: new, using · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, video, way · Missing: mobile apps, ios, entrepreneurs
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, friendly · Missing: plus, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
39%39% 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
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
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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