Video Notes for YouTube

Hacker News

I built a Chrome extension to take timestamped notes while watching YouTube. I learn a ton from YouTube, but taking notes with timestamps was always annoying so I built a simple extension for that. It's just a simple extension (plain HTML, CSS, JS). Everything is stored locally in your browser — no account, no sync. 60-second demo: https://www.youtube.com/watch?v=rOi7xQ8DLpo Chrome Web Store: https://chromewebstore.google.com/detail/video-notes/phgnkid...

Visit product View on Hacker NewsNov 23, 2025

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Actual performance

1points

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TrustMRRFits verified-revenue profile · Strong signals: video, google, way · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: google, notes, plain · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, 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

40%40% 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

26%26% 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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