Multiplex – An App to Watch Torrents with Your Friends

Hacker News

Hey HN! I just released Multiplex, an app to watch torrents together. It provides an experience similar to Apple's SharePlay and Amazon's Prime Video Watch Party, except for any torrent instead of a specific streaming service, and uses WebRTC to synchronize playback positions between the people watching without requiring a central server. It's written in Go and allows the use of a central HTTP to BitTorrent gateway[1] if a viewer can't use the BitTorrent protocol on their own device. I'd love to get your feedback :) [1] https://github.com/pojntfx/htorrent

Visit product View on Hacker NewsSep 7, 2023

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

55%55% 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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, way · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

42%42% 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

14%14% 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.

Correct prediction on native model

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