JayWebtech/autoshorts

▲ 93 stars today★ 1,016⑂ 193

AutoShorts is a local-first desktop application for turning long-form video or audio recordings into high-impact

About JayWebtech/autoshorts

JayWebtech/autoshorts is an open-source project on GitHub, mainly written in Rust. AutoShorts is a local-first desktop application for turning long-form video or audio recordings into high-impact It currently holds 1,016 stars and 193 forks with 0 open issues, and was last pushed on an unknown date (repository created unknown).

Project Overview

AI Homed tracks it on the Today's Trending board, currently at rank #28 with 93 new stars today.

GitHub Repository Details

Repository JayWebtech/autoshorts · default branch - · size 0 KB · watchers 0 · source: GitHub REST API and repository README

README

⭐ Support AutoShorts

AutoShorts

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.

https://github.com/JayWebtech/autoshorts/blob/HEAD/Screenshot 2026-06-22 at 4 10 13 PM

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Key Features

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Prerequisites

To run the application, FFmpeg & FFprobe must be installed and available on your system PATH to handle cropping, audio extraction, and dynamic captions:

  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:
  brew tap homebrew-ffmpeg/ffmpeg
  brew install homebrew-ffmpeg/ffmpeg/ffmpeg
  
  winget install Gyan.FFmpeg
  
(Or download the release build from gyan.dev and add it to your system PATH environment variables).
  sudo apt install ffmpeg      # Debian/Ubuntu
  sudo pacman -S ffmpeg        # Arch Linux
  sudo dnf install ffmpeg      # Fedora
  

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Installation Guide (For Users)

Download the correct package matching your system from the latest GitHub Releases.

🖥️ macOS Installation

1. Download: 2. Install: Double-click the .dmg file and drag AutoShorts to your Applications folder. 3. Bypass Gatekeeper (For unsigned local builds):
     xattr -cr /Applications/AutoShorts.app
     

🪟 Windows Installation

1. Download: Select the .msi (installer) or .exe (portable executable) package. 2. Install: Double-click the .msi file to run the setup wizard. 3. SmartScreen Bypass: Since the package is self-signed, Windows SmartScreen may show a warning. Click "More Info" in the window and choose "Run anyway".

🐧 Linux Installation

1. Download: Select the .deb (Debian/Ubuntu) or .AppImage (universal portable binary). 2. Install .deb:
   sudo dpkg -i autoshorts_*.deb
   
3. Run .AppImage: Make it executable and launch it:
   chmod +x autoshorts_*.AppImage
   ./autoshorts_*.AppImage
   

🚀 First-Launch Onboarding & AI Configuration

When you first launch the application, you will be greeted by an Onboarding Wizard that lets you choose your preferred workflow:

Option A: Fully Offline (Ollama + Whisper)

1. Ollama Setup: Select a local model card (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. 2. Local Whisper: Follow the prompt instructions to verify Python is installed and run pip3 install openai-whisper to enable fully offline transcription.

Option B: Cloud API Keys

1. Enter your API credentials for: 2. Click Save & Start to immediately load the dashboard.

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⚙️ Modifying Settings & Resetting Onboarding

[!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).

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Developer Guide

1. Setup Environment Configuration

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

2. Run in Development Mode

To start the live-reloaded frontend and backend development shell:
npm install
npm run tauri:dev

3. Build the Application

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/.

❤️ Support AutoShorts

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

GitHub Stars & Activity

1,016Stars
193Forks
0Open issues
RustLanguage

GitHub Popularity

GitHub stars1,016
Forks193
Open issues0
Primary languageRust
License-
Stars gained today93
Created-
Last pushed-

Trending History

Daily boardrank #28 · ▲ 93 stars
Weekly boardrank #49 · ▲ 170 stars

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