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A minimal Chinese learning site

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A minimal Chinese learning site

This is a non-AI Chinese learning app I started working on before ChatGPT. Now that LLMs are near-perfect translators, this non-AI approach is clearly not the global optimum, so I figured I might as well put it out before its utility hits 0. I made this to help myself speak Chinese to my parents. Great tools already existed for learning to _read_ Chinese, but I was unable to find anything designed to help you produce your own grammatically correct Chinese sentences. As any 2nd gen knows, the ability to understand a language does not imply the ability to speak/write it. So this app, Shuo Chinese, which I named affectionately after the reading app Du Chinese, starts by just showing you a bunch of random English sentences (it re-rolls upon refresh). For each sentence, you're supposed to try to translate it to natural Chinese. After you've come up with your answer, click the sentence to reveal the Chinese translation. From there, you can go deeper in two ways: 1. Hover over any character/word to reveal: a. The pinyin and definition b. The etymology of the character, to help you remember how to write it c. The HSK level of the character, to indicate how important it is for you to remember this character (HSK 1 means the character is super common so you should definitely know it) 2. Underneath each Chinese translation is some text describing the grammar pattern of the sentence. Click it to reveal an article that explains the pattern in depth and provides more examples. The app by default shows you medium-difficulty sentences, but you can adjust the difficulty to anything from A1 to C1 via the settings. All of the content is hand-written by the folks from AllSet Learning Chinese Grammar Wiki, so you can be sure it's authentic Chinese. I also generated an audio version of each sentence using GCP text-to-speech. Since LLMs have killed the possibility of me continuing work on this, its source code is yours: https://github.com/Xyzrr/shuo-chinese/tree/main Hope someone can still find value in this project! I put a lot of love into it.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
90%90% 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: chatgpt, using, code · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% 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: chat, audio · Missing: web3, crypto, cryptocurrency
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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