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Transfer files quickly from mobile to desktop via QR codes

Hacker News

Transfer files quickly from mobile to desktop via QR codes

Hey everyone, my friend from university and I built this simple app to handle a really specific use case: You need a picture or PDF transferred from your phone to your PC, and don't want to use whatsapp web / google photos / etc. Here's the workflow: 1. Open qrpigeon.pics on your desktop browser 2. Open your phone camera, scan the QR code, and get redirected to qrpigeon (connected to the desktop's session) 3. Upload / take a photo or PDF from your phone via the phone's browser 4. See the file appear instantly on the desktop browser! Please give us feedback! We want to make this as good as possible, and potentially get some open source contributions :) https://github.com/leiDnedyA/qr-image-drop/

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
66%66% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, code, open · Missing: mac, agents, macos
65%65% 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: open source, io · Missing: https docs, excited, just released
49%49% 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
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
45%45% 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
23%23% 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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