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I built an embeddable Unicode library with MISRA C conformance

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I built an embeddable Unicode library with MISRA C conformance

Hello, everyone. I built Unicorn: an embeddable MISRA C:2012 implementation of essential Unicode Algorithms. Unicorn is designed to be fully customizable: you can select which Unicode algorithms and character properties are included or excluded from compilation. You can also exclude Unicode character blocks wholesale for scripts your application does not support. It's perfect for resource constrained devices like microcontrollers and IoT devices. About me: I quit my Big Corp job a few years back to pursue my passion for software development and this is one of my first commercial releases.

Visit productView on Hacker NewsDec 15, 2024
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
57%57% 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
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos
27%27% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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