A YouTube videos course generator

Hacker News

REPOST! (to get more feedback from HN) An exciting aspect of what I'm working on is that users can create their own playlists. For example, you can provide a description of what you're learning, such as a lesson title, and the pipeline will create a youtube playlist. Since the content is highly customized, it will be relevant and high-quality, matching current lessons or lectures in school or university. I'm trying to find a product structure that users will find valuable and effective. The web app is completely free to use right now as I figure out the best model.

Visit product View on Hacker NewsSep 12, 2024

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Actual performance

3points

1comments

Did not reach leaderboard

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

60%60% 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: lua, ide, pipe · 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: users · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

TrustMRRLess likely to generate early MRR · Strong signals: video, users · 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

18%18% 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.

Incorrect prediction on native model

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