Password-protect a file with Easy Encryption Everywhere

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My friend needed to send credit-card data to a client and couldn't find a widely available program for sending password-protected files. So, I wrote one. This webpage encrypts a file and packages it into an HTML file, which contains JavaScript code to accept a password and decrypt the data. The code uses the Web Cryptography API and works with every major browser released since 2015. I'd love feedback. Especially from experts on cryptography and the Web Cryptography API.

Visit product View on Hacker NewsMar 13, 2024

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Launch Intel predictions

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

76%76% 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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%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: crypto · Missing: web3, chat, cryptocurrency

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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