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FileVeil · Hide Any File Inside Another File
Hacker NewsFileVeil · Hide Any File Inside Another File
I built FileVeil to hide any file inside another file. Use a photo, video, document, audio file, or archive as the cover file. The resulting file still looks and works normally while securely containing encrypted hidden files. Everything runs locally in your browser. No uploads. No servers. AES-256-GCM encryption. Video demo: https://www.youtube.com/watch?v=tcxjIDRZh9s I'd love your feedback on the implementation, UX, and real world use cases.
Share cardActual performance
3points
3comments
Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos60%60% 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: ide, io · Missing: https docs, excited, just released53%53% 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, intuitive47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal32%32% 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, revenue18%18% 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, real world · Missing: web3, chat, crypto3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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