Website That Generates "Pseudo-Chinese (偽中国語)" Text with LLMs

Hacker News

Hey HN! I built a fun little website that generates "Pseudo-Chinese (偽中国語)" using an LLM. It takes your input and transforms it into a sentence composed only of Kanji or Chinese characters, but structured with Japanese grammar. This makes it surprisingly readable to both Japanese and Chinese speakers who can leverage their knowledge of shared characters. The inspiration for this project came from another popular Japanese app called "対多." Its developer mentioned struggling to achieve satisfactory "Pseudo-Chinese" generation using ChatGPT. This piqued my curiosity, and I decided to tackle the challenge myself. I'd love for you to give it a try and let me know what you think!

Visit product View on Hacker NewsDec 21, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · 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 · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

43%43% 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, io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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 · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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