PikaTorrent, a modern, multi-platform, open source BitTorrent app

Hacker News

I find existing BitTorrent clients offer a rather old user interface, and it's hard to set up remote access, involving setting up a web server, a TLS certificate, domain name, configuring the router, etc... PikaTorrent tries to offer a modern and easy UI. It is available on desktop (Linux & Windows for now), mobile (Android for now), as CLI as an npm package, and even on the web (as a frontend for remote control). Thanks to WebRTC, one can link the mobile or web app with the desktop or CLI app. This way, it's possible to remotely manage torrents securely and without any complicated setup. From a technical point of view, the app is using Expo and Tamagui to target the web, desktop, and mobile (native), meanwhile libtransmission is used as the torrent engine. So we should expect the same level of performance as the Transmission client. PikaTorrent is open source, and I just released v0.3.0 to let potential users try it out and contribute to the bug/features list on GitHub: https://github.com/G-Ray/pikatorrent . Thank you for your feedback.

Visit product View on Hacker NewsJul 15, 2023

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, interface, users · Missing: plus, intuitive, reviews

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

Hacker NewsStrong engagement from HN community · Strong signals: just released, exist, open source · Missing: https docs, excited, lua

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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