uvx qrdrop – instant LAN file sharing from your terminal

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I’m working on some projects that involve moving files between macOS, Linux, Windows, Android, and iOS, and wanted a no-fuss, cross-platform way to do it over the LAN. I already have uv installed everywhere, so built qrdrop to run with uvx. From any directory you want to share, run: `uvx qrdrop` It starts a temporary HTTP server on your local network and gives you a URL, a generated three-word password, and a QR code. Scan the code on your phone and you can be browsing, uploading, downloading, renaming, and deleting files through the web UI in seconds. (note: full upload/download/modify was what I originally needed and remains the default, but I added --upload and --readonly modes if you'd like a more restrictive experience)

Visit product View on Hacker NewsJun 28, 2026

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

71%71% 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: ios · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

51%51% 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: ios, way · Missing: mobile apps, personal, entrepreneurs

35%35% 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

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

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

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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