XID – Dependency-Free Unique ID Generator in One C File

Hacker News

XID generates 20-character globally unique IDs (48-bit timestamp + 64-bit entropy + 8-bit counter + 5-bit checksum) in a single C file with zero dependencies. Features built-in timestamp extraction, collision resistance, and portable design for any platform - ideal for databases, IoT, and distributed systems. char id = xid_generate(); // => "01H5Z7K4W9A3B6C8D0F2G" Gists: https://gist.github.com/Ferki-git-creator/17450d52d143f746f0... (Feedback wanted: entropy improvements, embedded use cases, optimizations)*

Visit product View on Hacker NewsJul 20, 2025

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

63%63% 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: ide, 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 · Strong signals: platform · Missing: plus, intuitive, reviews

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: single · Missing: mac, agents, macos

30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

28%28% 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

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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