Python – A command-line utility for automating subtitle downloading

Hacker News

A command line utility that allows you to download subtitles for your media files i.e., Anime, TV-Series and Movies. It's written in python, fast and extremely light-weight. It's time-consuming to open a browser, search subtitles for a movie and then download them. Subgrab is a utility that allows you to automate this process and save time. Allows you to download subtitles for a custom movie, movies contained in a directory, specify language in which the subtitles should be downloaded and specify number of subtitles to be downloaded for a file. It's open-source and can be found at:- https://github.com/RafayGhafoor/Subscene-Subtitle-Grabber Currently supports subtitles downloading from two sites which are subscene and allSubDB. Suggestions/Critiques and contributions are welcome :).

Visit product View on Hacker NewsSep 17, 2017

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6points

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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

52%52% 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

43%43% 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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos

34%34% predicted probability of success on Product Hunt, 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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