SendIt – A privacy-first file sharing app with link and access control

Hacker News

Hi HN , I just launched SendIt , a file sharing app focused on privacy, flexibility, and control. Unlike services that treat file sharing as one-size-fits-all, SendIt lets you tailor exactly how and when your files are accessed. ## Features - *Effortless File Uploads* – Drag & drop or select files to upload instantly with a smooth, responsive interface. - *Instant Link Generation* Share files via: - *Normal links* – Random 7-character string - *Friendly links* – Auto-generated using adjectives + nouns + numbers - *Custom links* – Choose your own slug (validated and checked for availability) - *QR Code Sharing* – Instantly generate a QR code for any file link to share effortlessly across devices. - *File Expiry Control* Keep your files temporary with: - *Time-based expiry* – Auto-deletes after a set duration - *Download limits* – File is removed after set number of downloads - *One-time access* – Deleted right after the first successful access - *Advanced File Protection* Choose how securely your files are accessed: - *Public* – Anyone with the link can access - *Password-protected* – Recipient must enter a shared password - *Email-protected* – Only logged-in authorized users (approved by sender) can access - *OTP-protected* – File access granted via one-time password sent to the recipient’s email (no login required) GITHUB: https://github.com/aayushmaan-54/SendIt

Visit product View on Hacker NewsMay 26, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly, interface, users · Missing: plus, platform, intuitive

47%47% 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

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, email, using · Missing: mac, agents, macos

36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

32%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, revenue

13%13% 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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