FastQR – A Fast QRCode Generator Supporting Batch Processing

Hacker News

I'd like to share FastQR ( https://github.com/tranhuucanh/fastqr ), a high-performance QR code generator written in C++. This is my first open-source project, and I'm excited (and a bit nervous!) to share it with you all. What it is: - A fast CLI tool and library for generating QR codes - Written in C++ with bindings for Ruby, PHP, and Node.js - Full UTF-8 support (works great with Vietnamese, Japanese, and other languages) - Supports custom colors, logo embedding, and precise size control - Pre-built binaries included – no need to install dependencies separately Why I built this: - I needed a QR code generator that was fast, supported UTF-8 properly (especially for Vietnamese text), and could be easily integrated into different languages. Most existing solutions were either slow or had poor Unicode support. Performance: - Generating 1000 QR codes (500x500px): ~0.37 seconds Tech stack: - C++ core using libqrencode and libpng - Language bindings for Ruby, PHP, and Node.js - Precompiled binaries for easy installation This is my very first open-source project, so I'm sure there are things that could be improved or bugs I haven't caught yet. I'd really appreciate it if you could try it out and share your feedback. If you find any issues or have suggestions, please open an issue on GitHub – I'll do my best to fix them quickly. Any feedback, criticism, or suggestions would be greatly appreciated. Thanks for taking the time to check it out! GitHub: https://github.com/tranhuucanh/fastqr

Visit product View on Hacker NewsOct 21, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

65%65% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: using, code, open · Missing: mac, agents, macos

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

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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

16%16% 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.

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