TrguiNG – GUI for transmission torrent daemon

Hacker News

I just published first version of TrguiNG https://github.com/openscopeproject/TrguiNG Builds for all platforms are available in release page. I consider it to be in beta: stable, functional and usable but may need some polish here and there. Please report any issues you may find if you try this app. Here are some features that make it stand out from most clients: * Multi tabbed interface for concurrent server connections (native app only) * Torrent creation with fast multi threaded hashing (native app only) * Powerful torrent filtering options * Latest transmission features support: labels, bandwidth groups, sequential download * Dark and white theme * Works as windows/linux/mac app or as a web interface served by transmission itself

Visit product View on Hacker NewsJun 16, 2023

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

78%78% 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: mac, open · Missing: agents, macos, agent

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

36%36% 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

21%21% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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