Create Your Own TV Channel

Hacker News

Hey HackerNews'ers, I've built FreeTVz.com - a platform that transforms YouTube into a traditional TV experience. Here's what you can do: ◉ Create your own TV channels based on any topic or interest. ◉ Browse and watch over 100+ free curated channels across categories like History, Science, Tech, and more. ◉ Enjoy an ad-free experience, making your viewing uninterrupted and seamless. ◉ Rediscover the nostalgia of channel surfing, without endless scrolling or algorithmic feeds. I'd love for you to check it out and share your thoughts. Visit FreeTVz.com

Visit product View on Hacker NewsOct 31, 2024

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Actual performance

13points

16comments

Made the leaderboard

Launch Intel predictions

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

65%65% 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.

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

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

nativeThis product was originally launched on this platform.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

16%16% 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 · 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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