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I made a website that converts YT videos into step-by-step guides

Hacker News

I made a website that converts YT videos into step-by-step guides

Hey HN, I've been working on this side project for the past month. It generates a step-by-step tutorial guide for YouTube videos that you can follow along without watching long videos. Best suited for tutorial videos but can work for other videos aswell. No BS. Just straight to the point. The guides are generated from pure transcript so you don't have to worry about it being AI. It's my first project as a total beginner. Something I had to do inorder to get out of tutorial hell. Please let me know if you have any suggestions or if you face any problems or bugs. I would try to fix them to the best of my abilities and as soon as possible. I would appreciate your feedback on this. Let me know what you think!

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
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.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
46%46% 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: soon · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal
39%39% 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
22%22% 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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