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Protect files with AES-256 encryption in your browser

Hacker News

Protect files with AES-256 encryption in your browser

Hi, my name is Ethan, and I'm the maker of ZipOne. For many years, I've been using encryption apps and even 7-Zip to protect my important information, such as taxes, private photos, and other important paperwork. But none of these apps fully meet my needs. They are either too complicated, not usable on different platforms, or too slow. After several painful migrations between encryption apps, I decided to fix this. ZipOne is an easy-to-use browser-based encryption application that offers secure, fast, cross-platform encryption. It has much built-in security functionality so that you can protect your sensitive files well with just ZipOne. Select the files you want to protect or enter the information you want to encrypt. ZipOne applies AES-256 encryption to your data and locks them into an encrypted archive in seconds. Once an encrypted archive is created, you can safely save it anywhere or send it securely over the internet. When you need to retrieve files from an encrypted archive, you can quickly preview the archive's contents without first decrypting the entire contents of the archive to the device, and you can retrieve only the files you need. It's worth mentioning that ZipOne doesn't directly use your password as an encryption key to encrypt data. ZipOne uses a hybrid cryptosystem, similar to how some password management apps work but different from most file encryption apps. This makes ZipOne easy enough to use without sacrificing security. Thanks to hybrid encryption, you can change your password anytime without re-encrypting previously encrypted files, or you can share encrypted data with others without sharing a common password. Try it out, and send me any feedback.

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, using · Missing: mac, agents, macos
81%81% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
38%38% 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
12%12% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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