Building a YT Front & Back End from Scratch

Hacker News

Hi all, well its been a labour of love. I have much more appreciation for the youtube devs after trying to recreate my own version. The benefits of SkipVids.com : 1) No Ads. Skips sponsorship messages too thanks to Sponserblock API 2) Free Background Playback 3) Free PiP (picture in picture) 4) Lite - it uses about 1/5 the resource of YouTube 5) Private - we store nothing 6) View Dislike count, thanks to RYD API (not 100% accurate) 7) Casting is supported when using SkipVids.com on desktop 8) and so much more Give it a go, its not perfect but its pretty good IMHO.

Visit product View on Hacker NewsNov 27, 2024

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

68%68% 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 NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

29%29% 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

25%25% 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

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

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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