AutoShorts is a local-first desktop application built with Tauri 2, React, Rust, and SQLite. It automatically parses long-form videos and audio recordings, detects viral moments using DeepSeek or Claude, transcribes audio, and crops 9:16 portrait clips with FFmpeg.

Can AutoShorts run 100% offline?

Yes. AutoShorts supports local Ollama models (Llama 3.2 3B, Qwen 2.5) for viral hook detection and local OpenAI Whisper for offline speech-to-text transcription.

AUTOSHORTS_CLIPPER.exe

<- Back to Resources

AutoShorts: Local-First AI Short-Form Video Clipper & Generator ⚡

How to extract viral vertical short clips (9:16) from podcasts, streams, and YouTube videos on your desktop using DeepSeek, Claude, and local FFmpeg.

⚡ TL;DR

AutoShorts replaces expensive cloud video clipping SaaS subscriptions with a local-first desktop application.
Powered by Tauri 2 + Rust + React + SQLite, it ingests video files, extracts audio, generates transcriptions with word-level timestamps, and leverages DeepSeek or Claude to score retention moments, outputting cropped 9:16 portrait MP4s with zero cloud rendering costs.
Master AutoShorts — the local-first open-source desktop app for turning long-form podcasts into viral 9:16 portrait clips with DeepSeek, Claude, and FFmpeg.

Key Architecture & Capabilities

Multi-LLM Ranking: Seamlessly switch between DeepSeek V3 (ultra-low cost cloud analysis) and Claude 3.5 Sonnet (premium narrative hooks).
100% Offline Mode: Run fully air-gapped using local Ollama (Llama 3.2 3B / Qwen 2.5) and local OpenAI Whisper.
Native FFmpeg Portrait Auto-Cropping: Converts 16:9 widescreen video into 9:16 vertical clips with speaker centering.
Local SQLite Database: Stores candidate timestamps, video metadata, and transcripts on your machine without cloud leaks.

MASTER PROMPT

1-Click Viral Podcast Moment Detector Prompt

Paste this prompt into Claude or DeepSeek alongside a timestamped transcript to extract top-ranked 9:16 clip candidates:

AUTOSHORTS_VIRAL_EXTRACTOR

Act as an expert Short-Form Video Producer and Viral Content Strategist. Analyze the following timestamped transcript and identify the top 5 highest-retention vertical clip candidates (duration 30 to 60 seconds each):

For each candidate, provide:
1. Exact Start & End Timestamps [MM:SS - MM:SS]
2. 3-Second Viral Hook Headline (The exact on-screen caption hook)
3. Viral Score (1-100) based on emotional resonance, contrarian insight, or practical utility
4. Visual Pacing Note (Where to insert B-roll, zooms, or kinetic text emphasis)
5. Outro Hook (Why the viewer will share, save, or loop the video)

Here is the transcript:
[PASTE TRANSCRIPT HERE]

GITHUB REPO autoshorts

View on GitHub ->

Local-first AI viral short video clipper built with Tauri 2 + Rust.

Prerequisites: Rust toolchain, Node.js 20+, FFmpeg, Ollama (optional for 100% offline)

# 1. Clone the autoshorts repository
git clone https://github.com/JayWebtech/autoshorts.git
cd autoshorts

# 2. Install frontend dependencies
npm install

# 3. Launch desktop app in development mode
npm run tauri dev

# 4. Or build production release binary
npm run tauri build

⚡ SCALING SHORT-FORM DISTRIBUTION?

I help founders build automated GTM operations, content repurposing loops, and audience growth systems.

Book a Growth Strategy Consultation ->