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Vizard – AI video editor repurpose long video to bite-size highlights

Hacker News

Vizard – AI video editor repurpose long video to bite-size highlights

Hello, Hacker News community! I’m Gary, the founder of Vizard.ai. I am thrilled to introduce you to our cutting-edge AI video editing tool that is designed to help content creators/marketers/businesses alike to repurpose long-form webinar/event/speech videos into bite-size social-ready highlights. As a beginner in video editing, I struggled with the complexity of professional video editing software like Adobe Premiere Pro or Apple Final Cut Pro. I found it overwhelming and challenging to use, and I would eventually abandon my efforts to edit videos. But I knew that video content creation was important, and I was determined to find a solution. Being an expert in AI, I decided to use my knowledge to simplify video editing. That's when I came up with the idea for Vizard.ai. I have trained several deep learning models that work together to simplify the video editing process. The first model is a computer vision model that segments the video into pieces. The second model is a speech-to-text model that transcribes the voice in the video. The last model is a scene change detector model that detects changes in the video. When I combined these three models, I was able to edit the video like editing a picture. I could trim the video by transcript text, making the process much simpler and more intuitive. Our tool is intuitive, easy to use, and designed to simplify the video editing process for beginners. With Vizard.ai, content creators, marketers, and businesses can quickly and efficiently repurpose long-form video content into bite-size social-ready highlights, increasing their reach and engagement on social media. I am excited to share my story and the story of Vizard.ai with the Hacker News community. Thank you for your time and consideration, and I look forward to your feedback and questions.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, computer · Missing: mac, agents, macos
90%90% 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 · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal
61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: intuitive, efficient · Missing: plus, platform, reviews
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, hacker news, ide · Missing: https docs, just released, exist
39%39% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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