An interactive demo of QR codes' error correction

Hacker News

Hi HN! This is a hobby project of mine that recently landed me my first interview and helped me get my first internship offers. Draw on a QR code, and the health bars will accurately display how close the QR code is to being unscannable. How few errors does it take to break a QR code? How many errors can a QR code handle? Counters at the bottom track your record minimum and maximum damage. (Can you figure out how to break a QR code with 0.0% damage to the actual data region?) Also, click on the magnifying glass button to toggle between "draw mode" and "inspect mode". I encourage you to use your phone's camera to scan the code as you draw and undo/redo to verify that the code really does break when the app says it does. I wrote the underlying decoder in C++, and it's compiled to WebAssembly for the website. I hope you find it interesting.

Visit product View on Hacker NewsApr 25, 2025

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

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best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

45%45% 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 · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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

10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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