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Signaller – the first public WebRTC signalling service using WebSockets

Hacker News

Signaller – the first public WebRTC signalling service using WebSockets

Hello HN! I love decentralisation for variety of reasons and have been recently playing with WebRTC. Whilst experimenting with it using simple-peer[0], I found signalling absurdly hard! There are public STUN servers[1] (of Google and Twilio) but there wasn't a single public signalling service available... until I created one! I wrote signaller, a WebSocket relay in Go, from scratch because all of the solutions I found was unnecessarily complex (such using socket.io) whilst plain WebSockets will do the trick. I believe one of the greatest advantages of decentralised applications is them not being a maintenance burden on the shoulders of their developers, in other words, the absence of a need to maintain an infrastructure to keep the app running (except maybe a web server for static files). Up until now, signalling was a barrier to use WebRTC, so by making signaller available as a public service I hope to enable more developers adopt and experiment with WebRTC. Feel free to use, but please don't abuse it. =) https://signaller.cecibot.com/ GitHub: https://github.com/boramalper/signaller ---- [0]: https://github.com/feross/simple-peer

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
81%81% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: google, single, using · Missing: mac, agents, macos
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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% 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
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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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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