Ge

Generate your own "movie trailer" with AI

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Generate your own "movie trailer" with AI

This is a toy project I've been hacking on, and it's a bit rough around the edges, but I'd love for people to try it out! https://aitrailers.xyz Some examples of videos people have generated: * An action movie starring a corgi: https://aitrailers.xyz/examples/paws-of-power * A children's movie about a mischievous monkey: https://aitrailers.xyz/examples/cheeky-monkey * And a "thriller" my friend Mark generated about how he lost one of his Airpods in a tissue box: https://aitrailers.xyz/examples/marks-lost-earbud So far it's been fun to see what people have come up with, but the quality of the output varies quite a bit... I'd love to get any feedback on the app so far, and also curious if anyone has any advice on the following: * What is the best way to improve character consistency across images? (I currently just try to construct each image prompt such that a vivid visual description of the main character and the overall aesthetic is the same across images, but I've had limited success with this.) * Have any other tools caught up to GPT-4 and DALL-E for things like this? (I've tinkered with some models on Replicate, but nothing is quite as good as OpenAI's APIs as far as I can tell.) * Are there any good "wrappers" for ffmpeg? I'm currently using a combination of ffmpeg and moviepy, but it surprises me that there isn't an "easier" way to generate videos. (Ffmpeg is super powerful, but it’s API is a bit unintuitive)

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% 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 HuntOn track for Day 1 leaderboard · Strong signals: model, models, visual · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: video, way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive · Missing: plus, platform, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
14%14% 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.

Incorrect prediction on native model

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