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Nousb.co – A seamless multi-platform ephemeral file transfer webapp

Hacker News

Nousb.co – A seamless multi-platform ephemeral file transfer webapp

Key technical point is that it does NOT rely on webRTC , which is not reliable enough on all browsers, and there are important file system APIs (ie. `showSaveFilePicker`) that are missing on most browsers to do actual file transfers, without storing the whole file in RAM first. The idea is to have something that is as multi-platform as possible, and can work on all browsers, even the embedded webviews in chat apps. And without app installation or account creation. To test it with a common case, which is transferring between a smartphone and a laptop: - Open https://NoUSB.co - Click the "Share link" button. - Scan the QR code with your phone or transfer the link to another computer. - Drag and drop large files; they'll instantly appear on the other side, regardless of size. - Click the download button on the receiving device. - Click the "Boost this!" button to unlock more transfer speed. - Close the tab and all sharing stop instantly Multiple devices can join the same virtual drive and share their own files. There's also a chat feature because if you click a sharing link from a messenger app, you will certainly end up trapped in their embedded webview with no way to get back to chat without disconnecting. For more technical details: https://dev.to/stant/nousbco-yet-another-file-transfer-tool-... Please test it and let me know how it went for you. Thanks! webRTC is actually implemented as an experimental feature, you'll need to add `&rtc=1` on both sides to enable it. Only chrome based browsers that implement `showSaveFilePicker` can be the downloading end.

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Actual performance

6points
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% 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: apps, computer, code · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
33%33% 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
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, smart · Missing: web3, crypto, cryptocurrency
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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