AI Generated Short Video

Hacker News

Hey everyone, I have always wanted to content. I enjoy creating videos and watching other's creations! Being a programmer, I decided to create an AI pipeline which can create textual scripts and churn out thousands of videos / blogs on a daily basis given any topic. This is my first video and I would appreciate any comments and suggestions regarding it but I am particularly interested in hearing about tips and tricks / content format which can be followed to make the such video more fun and intuitive to watch. Also looking for advice if this can be monetised maybe saas or ads or something completely different. I would also want to know if anyone of you would be interested in such an AI tool. The Video: https://www.youtube.com/watch?v=1J_DOIPGKsw Specifically, what I wanted to ask: What are the specific things you liked and what you didn't. Would there have been a better format for me to have made this video to deliver the message more effectively? Are there any concepts things I can look up that may help me improve my videos generation. Thank you again!

Visit product View on Hacker NewsOct 12, 2020

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

73%73% 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.

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

50%50% 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, pipe, io · Missing: https docs, excited, just released

45%45% 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: intuitive · Missing: plus, platform, reviews

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, way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

24%24% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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