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Hanzi Stroke – An interactive tool to learn Chinese character writing

Hacker News

Hanzi Stroke – An interactive tool to learn Chinese character writing

Hi HN, I’m building Hanzi Stroke ( https://www.hanzistroke.com ) to solve a very specific problem: learning to write Chinese characters is unintuitive for non-native learners. Most learners rely on rote memorization, which is inefficient and often leads to incorrect stroke habits. While there are dictionary apps that show static stroke order diagrams, they lack interactivity. The key to muscle memory is deliberate practice with immediate feedback. So, I made a single-page application that provides: Dynamic stroke order animations for over 9,000 characters. Interactive canvas practice where you trace characters on screen, stroke-by-stroke, with guidance. A structured curriculum based on the official HSK 1-9 vocabulary lists. Some technical aspects that might interest you: The frontend is built with Vue.js for a reactive experience, and the stroke data is parsed from a structured source. A core challenge was designing the stroke-matching logic to be both pedagogically correct and forgiving enough for learners. The goal was to create a focused, ad-free tool that does one thing well. This is a bootstrapped project born from my own experience teaching Chinese. It’s not another gamified language app; it’s a practical workbook reimagined for the web. I’d love for the HN community to try it, especially if you've ever been curious about learning Chinese. But more importantly, I'm keen to hear your thoughts: For learners: Does this approach make the writing process less daunting? For builders: How have you tackled similar challenges in making "traditional" skills interactive? All feedback is greatly appreciated. Thank you!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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, single, activity · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: intuitive, efficient, builder · Missing: plus, platform, reviews
55%55% 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
50%50% 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: apps · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: bootstrapped, active · 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 · 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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