ytcast – cast YouTube videos to your smart TV from command-line

Hacker News

hi, this is a project I've been working on and off for the last three months, it's my first non-trivial, non-hello-world, actually useful (at least to me) go project. it would be very nice if you could just try the program and see if it works on your setup! the readme[0] explains how it works, why I wrote it and how it fits in my "command-line centric" computing environment (there is also a video demo[1] :)). if you know go, feel free to take a look at the code and review it! I'm still pretty much new to go and I'd love to hear opinions, feedback and tips from more seasoned go developers :) [0]: https://github.com/MarcoLucidi01/ytcast#ytcast [1]: https://www.youtube.com/watch?v=07aWOpi8DVk

Visit product View on Hacker NewsFeb 16, 2022

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

68%68% 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.

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

61%61% 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 · Missing: supports, reddit linkedin, podcasting

55%55% 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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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