Video Composition Tool Powered by Qwen2.5-Coder and FFmpeg

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This tool allows you to drag and drop your own assets, such as videos, audio, and images, and then use natural language instructions to generate a new video. It uses the Qwen2.5-Coder-32B-Instruct model to process your assets and instructions, to generate a valid FFMPEG command. This command is then executed on your assets to create the desired video. What's particularly exciting with this is that it's powered by an open-source model licensed under Apache 2.0 ( https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct ). Tried to build something similar ~1.5 years ago, but at that time, it seemed only possible with proprietary models.

Visit product View on Hacker NewsNov 24, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos

91%91% 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

79%79% predicted probability of success on Indie Hackers, 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

66%66% 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 · Missing: mobile apps, ios, personal

58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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 · Strong signals: audio · Missing: web3, chat, crypto

9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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