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Instant E2E encrypted (large) file sharing from your terminal

Hacker News

Instant E2E encrypted (large) file sharing from your terminal

I built file.kiwi, a file sharing service where encryption happens entirely on the client side. (Encrypted Content-Encoding for HTTP,RFC 8188) CLI: filekiwi report.pdf filepath.any (or npx @file-kiwi/node report.pdf filepath.any ) It prints the shareable URL immediately with a QR code, shows real-time progress, and supports --resume for interrupted uploads. When piped, only the URL goes to stdout. Files are temporary (~90 hours), no accounts, no sign-up. no api-key needed. CODE: https://github.com/file-kiwi/node Would love feedback on the API design, CLI UX.

Visit product View on Hacker NewsMar 31, 2026

Actual performance

2points

1comments

Did not reach leaderboard

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best fitHighest predicted score across all platforms for this description.

Product HuntUnlikely to reach the leaderboard · Strong signals: coding, code · Missing: mac, agents, macos

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

Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, 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.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% 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

15%15% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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