AutoShorts is a local-first desktop application for turning long-form video or audio recordings into high-impact, vertical short-form clip candidates (9:16 portrait) with AI-powered viral moment ranking.
This repository implements the desktop app foundation using Tauri 2 + React + TSX + Rust + SQLite.
ffmpeg integration.To run the application, FFmpeg & FFprobe must be installed and available on your system PATH to handle cropping, audio extraction, and dynamic captions:
bash
brew install ffmpeg
Note: To ensure full captions rendering support, if standard Homebrew FFmpeg lacks drawtext/subtitles filters, tap and install the homebrew-ffmpeg formula:
bash
brew tap homebrew-ffmpeg/ffmpeg
brew install homebrew-ffmpeg/ffmpeg/ffmpegpowershell
winget install Gyan.FFmpeg
(Or download the release build from gyan.dev and add it to your system PATH environment variables).bash
sudo apt install ffmpeg # Debian/Ubuntu
sudo pacman -S ffmpeg # Arch Linux
sudo dnf install ffmpeg # FedoraDownload the correct package matching your system from the latest GitHub Releases.
aarch64.dmg package.x64.dmg package..dmg file and drag AutoShorts to your Applications folder.AutoShorts.app in Finder, select Open, and click Open in the warning dialog.bash
xattr -cr /Applications/AutoShorts.app.msi (installer) or .exe (portable executable) package..msi file to run the setup wizard..deb (Debian/Ubuntu) or .AppImage (universal portable binary)..deb:
bash
sudo dpkg -i autoshorts_*.deb.AppImage:
Make it executable and launch it:
bash
chmod +x autoshorts_*.AppImage
./autoshorts_*.AppImageWhen you first launch the application, you will be greeted by an Onboarding Wizard that lets you choose your preferred workflow:
llama3.2 3B, qwen2.5 3B, or qwen2.5 7B). The application will check if Ollama is running and automatically pull the model weights, showing a downloader progress bar.pip3 install openai-whisper to enable fully offline transcription.[!TIP] LLM Provider Recommendation (Local vs. Cloud): - Local Models (Ollama): While AutoShorts supports fully offline moments analysis via local Ollama models (like LLaMA 3.2 3B or Qwen 2.5 3B/7B), local models are generally not recommended for viral moment detection. Smaller 3B/7B models lack the context reasoning and mathematical capabilities needed to evaluate long transcripts and calculate accurate segment timestamps (often outputting fragments that are too short). - DeepSeek (Highly Recommended): We strongly suggest using DeepSeek for moment detection. It offers top-tier reasoning capabilities (matching GPT-4/Claude 3.5 Sonnet) at a fraction of a cent per run (under $0.001 per transcript). You can get an API key instantly at platform.deepseek.com. - Claude (Premium Option): Claude 3.5 Sonnet provides the absolute best hooks copywriting and emotional resonance, but is slightly more expensive than DeepSeek (typically $0.01 – $0.05 per run).
Copy .env.example to .env in the root folder:
cp .env.example .env
Fill in your API Keys:
DEEPGRAM_API_KEY=your-deepgram-api-key
DEEPSEEK_API_KEY=your-deepseek-api-key
ANTHROPIC_API_KEY=your-anthropic-api-key
# Choose your default AI analysis provider ("deepseek" or "claude")
LLM_PROVIDER=deepseek
To start the live-reloaded frontend and backend development shell:
npm install
npm run tauri:dev
To build and package the native macOS app bundle (.app and .dmg installer):
npm run tauri:build
The output installers will be built under src-tauri/target/release/bundle/.
If AutoShorts helps you create content faster, consider supporting its development.
Your support helps fund new features, bug fixes, and ongoing improvements.
👉 https://buy.polar.sh/polar_cl_ZjNgejG1JnPQqVXyMmEmq7vpdwJUFBqx4qahw4BqBCP
$ claude mcp add autoshorts \
-- python -m otcore.mcp_server <graph>