Tool for secure file sharing for your projects or personal use

Hacker News

Hey everyone, I created a tool called SimpleLock, which I’ve been developing to make file sharing both secure and simple, especially for individuals or small teams who handle sensitive data. It allows you to share files with password-protected links, ensuring that only the intended person can access the content. Everything is encrypted for security, so it’s perfect for anyone looking to protect their files with ease. Currently, the tool is in beta, and I’m testing its core features. Here’s what it offers so far: Perfect for Payslip Distribution: Share payslips or other sensitive documents securely without dealing with complicated ZIP files. End-to-End Encryption: Files are encrypted on upload with AES-256 encryption, so even the server can’t access them without the password. File Size Limit: For now, there’s a 256 KB limit for each file being shared. PDF Only: For now, only PDF files can be shared securely through the platform. Docker Support: If you're hosting your own infrastructure, you can deploy it using Docker.

Visit product View on Hacker NewsOct 28, 2025

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

70%70% 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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% 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: personal · Missing: mobile apps, ios, entrepreneurs

23%23% 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

10%10% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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