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Veo3.Directory – A curated collection of Google Veo3 videos

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Veo3.Directory – A curated collection of Google Veo3 videos

I've created Veo3.Directory, a platform that aggregates and showcases videos generated by Google's groundbreaking Veo3 video generation model. Veo3 represents a significant leap forward in AI video generation capabilities, producing remarkably realistic and creative content. As these videos have been appearing across the internet, I wanted to build a central repository where people could easily discover and explore the possibilities of this technology. The directory features: - Collections of Veo3-generated videos - Information about the prompts used (where available) - Regular updates as new examples emerge The directory is built using Next.js and Tailwind CSS, and is hosted on Vercel. The source code is available on GitHub: https://github.com/sing1ee/veo3-directory This is a passion project aimed at documenting the evolution of AI video generation and providing inspiration for creators interested in exploring this technology. I'd love to hear your feedback and suggestions for improving the directory!

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
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best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, new · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, google · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, 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
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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 · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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