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Updated: Inline YouTube Timestamps (RavenPoints.com)

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Updated: Inline YouTube Timestamps (RavenPoints.com)

Example inline timestamps - https://ibb.co/LQhBN5w I publically published the RavenPoints YouTube Chrome extension (www.RavenPoints.com) in a working MVP state. Several great ideas came from the feedback and I have been chugging through the requested features, namely: - don't require sign-in to see timestamps - put timestamps inline underneath YouTube videos - offer a karma bonus for people using referral codes Next up: I'm hoping to find some YouTubers that would be willing to chat with me about how they might use the extension and ways I could improve it to help them. If you know any YouTubers that might be interested, I'd love to chat with them. Feel free to DM me. Thanks!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
58%58% 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
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: using, code · Missing: mac, agents, macos
43%43% 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
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
37%37% 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
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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