Uprintf a universal stb-style printf implementation for C (no OS)

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I've been frustrated by the lack of a truly portable, no-dependency printf for embedded and kernel development. Most solutions are either too bloated or missing key features. So I built Uprintf. It's a single-header library that gives you full printf (flags, width, precision, floats, even custom specifiers) from bare metal to desktop, with zero dependencies or #ifdef hell. Key features: · One header file, no dependencies, no dynamic allocation · Full standard support: %d, %x, %f, %.*s, etc. · Extensible with custom format handlers (add %T for your project) · Configurable: disable floats, set locale, etc. · MIT Licensed. GitHub: https://github.com/Ferki-git-creator/Uprintf I'd love your feedback and contributions!

Visit product View on Hacker NewsAug 20, 2025

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

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

52%52% 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

29%29% 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

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

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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