Edit Mind: Revolutionizing Video Editing with AI-Powered Indexing

Have you ever spent hours searching through hundreds of hours of video footage for that one specific shot? What if you could search through your video library as easily as you search through documents? Edit Mind is an innovative solution designed to solve this exact problem. This cross-platform desktop application serves as an “editor’s second brain,” using artificial intelligence to locally process your video library and make every scene searchable and manageable.

What is Edit Mind?

Edit Mind is an AI-driven video indexing and semantic search platform. It analyzes your video files on your local device, extracting rich metadata including complete transcripts, recognized faces, dominant colors, detected objects, and on-screen text. All this information is consolidated into a fully searchable, offline-first video database, allowing you to find the exact shot you need within seconds.

Unlike traditional video management software, Edit Mind doesn’t rely on file names or manual tagging but understands the actual content of your videos. Whether you’re a professional video editor, documentary filmmaker, or someone with extensive personal video collections, this tool can significantly enhance your video retrieval efficiency.

Core Functionality Explained

Edit Mind offers a powerful set of features that redefine what’s possible in video content management:

Deep Content Indexing

While traditional video management depends on manual tagging or filename searches, Edit Mind uses AI analysis to automatically extract deep information from videos. The system recognizes faces, objects, color compositions, and even on-screen text while generating complete dialogue transcripts. This means you can search for “person in blue shirt” or “scenes containing cars” rather than just “vacation video 1.”

Semantic Search Capabilities

One of Edit Mind’s most impressive features is its natural language search capability. You don’t need to learn complex query syntax—just describe what you’re looking for in everyday language. For example, you can search for “show me all clips where Ilias looks happy” or “scenes with two people talking at a table,” and the system will understand your intent and return relevant results.

AI-Generated Rough Cuts

Beyond finding specific shots, Edit Mind can automatically assemble video sequences based on natural language descriptions. Simply tell the system what type of scenes you want, and it will find matching clips and generate a preliminary edit, saving significant time in your editing workflow.

Privacy-First Design

In an era where data privacy is increasingly important, Edit Mind adopts a “privacy by design” approach. All video files, frames, and extracted metadata remain completely on your local device. Only search prompt interpretation and text embedding generation require cloud API calls, while raw video content never leaves your device.

Cross-Platform Compatibility

Built on the Electron framework, Edit Mind runs seamlessly on macOS, Windows, and Linux systems, providing a consistent user experience regardless of your operating system.

Extensible Architecture

Edit Mind uses a plugin-based architecture that allows developers to easily extend its analytical capabilities. Existing plugins include object detection, face recognition, and shot type analysis, with future possibilities for audio event detection, logo recognition, and other new functionalities.

Technical Deep Dive

How does Edit Mind achieve these impressive capabilities? Let’s explore its technical workings in detail:

Video Analysis Pipeline

When you add a video to Edit Mind, it executes a comprehensive AI analysis pipeline:

1. Audio Transcription: The system uses OpenAI’s Whisper model to locally process video audio tracks, generating timestamped complete transcripts. This means all dialogue in videos becomes searchable text data.

2. Scene Segmentation: Videos are automatically divided into 2-second “scene” units, enabling frame-level precision indexing. This granular segmentation ensures search result accuracy.

3. Deep Frame Analysis: Each scene undergoes analysis through a series of Python plugins, including:

   • Face recognition: Detecting and identifying faces appearing in videos
   • Object detection: Identifying various objects within scenes
   • Optical Character Recognition (OCR): Extracting text displayed on screens
   • Color and composition analysis: Determining dominant colors and visual characteristics

4. Data Consolidation: The system aligns spoken text with visual content using timestamps, creating unified contextual indexing.

5. Vector Embedding and Storage: All extracted data (transcripts, tags, and metadata) are converted into vector representations using Google text embedding models and stored locally in the ChromaDB vector database.

6. Semantic Search Parsing: When you search using natural language, Edit Mind uses Google Gemini 2.5 Pro to convert your search prompt into structured JSON queries, which are then executed against the local vector database to retrieve relevant scenes.

Technical Architecture Details

Edit Mind’s technology stack is carefully selected to balance performance, maintainability, and cross-platform compatibility:

• Application Framework: Uses Electron as the foundation, allowing desktop application development with web technologies
• Frontend Interface: Built on React, TypeScript, and Vite, combined with shadcn/ui component library and Tailwind CSS styling framework for a modern, responsive user experience
• Backend Services: Node.js handles main application logic, while Python manages AI/ML analysis tasks
• AI/ML Components: Leverages open-source libraries like OpenCV, PyTorch, and Whisper for video analysis and transcription
• Vector Database: ChromaDB serves as local vector storage, supporting efficient similarity searches
• Build Tools: Uses Electron Builder for packaging applications, ensuring consistent cross-platform distribution

Installation and Usage Guide

System Requirements

To run Edit Mind, your system needs to meet these requirements:

• Node.js v22 or higher
• Python 3.9 or higher
• Recommended hardware: Multi-core CPU, modern GPU, and at least 8GB RAM

For large-scale video processing, more powerful hardware configurations will significantly improve performance. The performance benchmarks section below provides more detailed hardware requirement references.

Installation Steps

Edit Mind’s installation process involves several key steps:

# Clone the repository
git clone https://github.com/iliashad/edit-mind
cd edit-mind

# Install Node.js dependencies
npm install

# Set up Python environment
cd python
python3.12 -m venv .venv                                                  
source .venv/bin/activate   # macOS/Linux systems
# .\.venv\Scripts\activate  # Windows systems
pip install -r requirements.txt
pip install chromadb

# Start ChromaDB vector database
chroma run --host localhost --port 8000 --path .chroma_db

API Key Configuration

Edit Mind requires a Google Gemini API key to process natural language search queries. Create a .env file in the project root directory and add the following content:

GEMINI_API_KEY=your_api_key_here

Obtaining an API key requires visiting Google AI Studio and creating appropriate API credentials. Note that this is currently the only component requiring cloud services, with future versions planning to offer completely offline alternatives.

Launching the Application

After completing the above setup, you can start the Edit Mind application:

npm run start

Production Build

To create distributable application packages, use the build command:

npm run build:mac

This will generate operating system-specific installers or executables in the out/ directory according to the electron-builder.yml configuration file.

Performance Characteristics and Optimization Recommendations

Understanding Edit Mind’s performance profile is crucial for using the tool effectively. Here are performance data based on actual tests conducted on an M1 MacBook Max (64GB RAM):

Performance Benchmark Data

The following table shows performance metrics when processing different video files, with all tests using the complete plugin suite (object detection, face recognition, shot type analysis, environment analysis, and dominant color analysis):

Key Performance Findings

From the test data, we can draw several important conclusions:

• Processing Speed: On average, Edit Mind requires approximately 2-3 hours to analyze 1 hour of video content (with all plugins enabled). The “2.29×” in the processing rate column indicates that the analysis time is 2.29 times the actual video duration.

• Memory Consumption: Peak memory usage ranges between 5-11GB, depending on video complexity and codec. Videos with HEVC codec show more variable performance characteristics, possibly due to encoding parameters and scene complexity.

• Codec Impact: Different video codecs significantly affect performance. H264 and HEVC were the main codecs tested, with HEVC demonstrating greater performance variation across different files.

Practical Optimization Suggestions

Based on these performance data, the following recommendations can help optimize your Edit Mind experience:

1. Selective Plugin Activation: If you don’t need certain analysis features (like color analysis or object detection), disabling corresponding plugins can significantly reduce processing time and memory usage.

2. Strategic Processing Scheduling: For large video files, consider processing during times when you don’t need to use your computer, such as overnight or on weekends.

3. Hardware Considerations: To ensure smooth operation, having at least 16GB RAM is recommended. Using SSD storage can significantly improve I/O performance during analysis.

4. Video Format Selection: When possible, using videos with standard H264 codec may provide more consistent performance.

Project Status and Future Development

Edit Mind is currently in active development and not yet production-ready. Users may encounter incomplete features or occasional bugs. The development team welcomes community contributions to help the project reach its v1.0 milestone.

Near-Term Development Roadmap

v0.2.0 Version Plans:

• Advanced search filters (date ranges, camera types)
• Export rough cuts as Adobe Premiere Pro and Final Cut Pro projects
• Improved indexing performance

v0.3.0 Version Plans:

• New analysis plugins (such as audio event detection)
• Plugin documentation and examples

Long-Term Development Vision

Looking ahead, Edit Mind plans to introduce more innovative features:

• Optional index cloud synchronization
• Collaborative tagging and shared libraries
• Plugin marketplace
• Completely offline operation mode, eliminating dependency on any cloud APIs

Frequently Asked Questions

How does Edit Mind handle my privacy and data security?

Edit Mind employs a “privacy-first” design. All video files, frames, and extracted metadata remain completely on your local device. Only search query interpretation and text embedding generation call the Google Gemini API, but raw video content is never uploaded to the cloud. Future versions plan to offer completely offline alternatives.

What hardware configuration do I need to effectively use Edit Mind?

A system with a multi-core CPU, modern GPU, and at least 8GB RAM is recommended for optimal performance. For large video libraries or high-resolution content, 16GB+ RAM and SSD storage will significantly improve the experience. Specific performance metrics can be found in the performance benchmarks section of this article.

Which video formats does Edit Mind support?

Edit Mind supports various common video formats, depending on the capabilities of underlying processing libraries (like OpenCV and Whisper). Testing has successfully processed video files with H264 and HEVC codecs.

Can I use Edit Mind on multiple devices?

Yes, Edit Mind is a desktop application that can be installed on multiple devices. However, the current version doesn’t include built-in synchronization features, so indexes on each device are independent. Future versions plan to offer optional cloud synchronization.

How can I extend Edit Mind’s analysis capabilities?

Edit Mind uses a plugin-based architecture that allows developers to create new analysis plugins. Existing plugins are located in the python/plugins/ directory and can serve as references for new plugin development. Community contributions for new plugins (like logo detection, emotion analysis) are highly welcome.

How does Edit Mind differ from Adobe Premiere Pro/Final Cut Pro?

Edit Mind isn’t a replacement for video editing software but rather a complementary tool. It focuses on helping users quickly find relevant shots within large amounts of footage, after which selected clips can be exported to professional editing software for fine-tuning. Future versions will support direct export as Premiere Pro and Final Cut Pro projects.

Project Structure and Technical Contributions

Edit Mind’s codebase adopts a modular structure ensuring good maintainability and extensibility:

• app/: Contains all React frontend code (pages, components, hooks, styles), corresponding to the renderer process
• lib/: Contains core Electron application logic

  • main/: Electron main process entry point and core backend services
  • preload/: Preload scripts for securely bridging main and renderer processes
  • conveyor/: Custom-built type-safe IPC (Inter-Process Communication) system
  • services/: Node.js services coordinating tasks like calling Python scripts
• python/: Contains all AI/ML analysis, transcription, and other Python scripts
• resources/: Static assets not part of the web build, such as application icons

This structure ensures separation of concerns, enabling developers to easily understand and extend different parts of the codebase.

Getting Involved

As an open-source project, Edit Mind welcomes contributions in various forms:

• Reporting Issues: If you find bugs, please submit issue reports
• Improving User Interface: Have ideas for interface improvements? The team would love to hear your suggestions
• Developing Plugins: The analysis pipeline is built on plugins. If you have ideas for new analyzers (like logo detection, audio event classification), you can refer to existing plugins in the python/plugins/ directory

The project follows standard open-source contribution processes. Details can be found in the CONTRIBUTING.md file in the project repository.

Conclusion

Edit Mind represents an innovation in video content management, using artificial intelligence to make video searching as intuitive as text searching. Although the project is still in development, its existing features demonstrate significant potential.

Whether you’re an editor handling professional video projects or an individual managing extensive personal video collections, Edit Mind can save you valuable time by quickly locating needed content. As the project matures and community contributions grow, we can anticipate a more refined, feature-rich video indexing solution.

The project’s open-source nature means anyone can participate and help shape the future of video management. Visit Edit Mind’s GitHub repository to start exploring this innovative tool or bring your ideas and contributions to the development team.