Wu

Wush.dev – easy P2P file transfer and shells between browser and CLI

Hacker News

Wush.dev – easy P2P file transfer and shells between browser and CLI

A few months ago I wrote a command-line based P2P file transfer and remote access tool called wush [1], which got some pretty good traction. I decided to extend the functionality to bridge the gap between the browser and CLI, allowing file transfers and shells from anywhere. It was built without a trusted coordinator server, with all P2P negotiation happening over DERP [2] using a shared x25519 key. The original CLI was built using the open source version of Tailscale, which was ported into WASM pretty easily. Unfortunately, Tailscale on WASM is unable to get P2P connections since browsers won't just let you send arbitrary UDP packets. In tests I was barely able to get above 1-2 Mbit/s, which led to the decision to switch to WebRTC for browser <-> browser and browser <-> cli file transfers. This is my first big front-end/browser project and I definitely learned a lot. Tailscale is impressively flexible and I wouldn't have been able to do this without their progress. Happy to answer any questions and discuss in the comments. Thanks! [1] https://github.com/coder/wush [2] https://tailscale.com/kb/1232/derp-servers

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Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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Product HuntOn track for Day 1 leaderboard · Strong signals: using, code, open · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
39%39% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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