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FingerprinterJS – A tiny JavaScript library for browser fingerprints
Hacker NewsFingerprinterJS – A tiny JavaScript library for browser fingerprints
I made FingerprinterJS, a small library with no dependencies that creates browser fingerprints from signals like canvas, WebGL, audio, fonts, userAgent, and screen info. It’s written in TypeScript, lets you enable/disable collectors, add custom data, and includes a simple suspicious-activity score. Would love feedback.
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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, activity · Missing: mac, agents, macos91%91% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive45%45% 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, personal38%38% 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, revenue16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto15%15% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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