edit-mind

hub stars Now, I'm building a desktop app with direct NLE integration (Final Cut Pro, DaVinci Resolve, and Adobe Premiere Pro). This includes an editing agent that understands your footage and your editing style. (https://edit-mind.com) Demo Video: https://youtu.be/jcctyfVg_34

hub stars Now, I'm building a desktop app with direct NLE integration (Final Cut Pro, DaVinci Resolve, and Adobe Premiere Pro). This includes an editing agent that understands your footage and your editing style. Preview: https://youtu.be/jcctyfVg_34 Happy to answer questions and hear your feedback.

UI and have a search and chat feature. Then, I was like let's open source it and share it with the community over Reddit. People loved it (https://www.reddit.com/r/selfhosted/comments/1ogis3j/i_built...). Many of them requested Docker integration. I decided to focus on that instead, which was a great idea and suggestion. (https://www.youtube.com/watch?v=YrVaJ33qmtg&t=12s) Now, we have 3 Docker containers: one responsible for the web UI, one for the background jobs , media stuff and local vector integration, one for the ML service (transcription and frame analysis) that will be a Python script communicating via WebSocket. After posting over X and tagging Twelve Labs for inspiring me to add new features and UI enhancements to the project, I had the opportunity to present the project live at their webinar series (if you wanna check Edit Mind in a live demo: https://www.youtube.com/watch?v=k_aesDa3sFw&t=1271s) After getting the proof of concept with all the features that I was hoping to get from Edit Mind, it's time to focus on improving the code quality, refactoring, and implementing best practices. Remember that I'm working solo with the help of external contributors. I would love to get your feedback about the project. GitHub: https://github.com/iliashad/edit-mind

r database) - Natural language gets parsed into structured queries, then semantic search finds matches (uses Gemini API currently, but can swap in a local LLM like Ollama if you prefer) What it actually does: 1. Type: "scenes where @Ilias is looking happy, eating a pizza" 2. Behind the scenes it converts this to: {"faces":["Ilias"], "emotions": ["happy"], "objects": ["pizza"]} 3. Then searches your local vector database for matching scenes across 2TB of footage in seconds. Real cost comparison: * GCP Video Intelligence API: ~$0.10/minute of video (https://cloud.google.com/video-intelligence/pricing) = $100+ for 200 (5 minutes) videos (I have over 3000 videos), this will cost me over 1500+$ for getting the videos analyzed * Edit Mind: Free after initial setup, runs on your own hardware (you need to have good one to handle this heavy video processing tasks) Technical choices: Built with Electron because I needed real filesystem access and didn't want browser storage limits. Python backend handles the heavy ML work (face_recognition, YOLOv8, FER for emotions) and communicate via web sockets. The analysis pipeline is plugin-based – took me less than a day to add color dominate as a separate plugin. Current limitations: * Needs decent hardware (GPU recommended but not required) * Face recognition requires some manual training (adding known faces) * UI is functional but could be prettier * Query parsing uses Gemini API by default (but you can configure it to use local alternatives) Why I'm sharing this: I can't be the only person with this problem. Videographers, parents with years of family footage, documentary filmmakers, anyone with large video libraries. The code is MIT licensed and designed to be extended. Would love feedback on: 1. Is $450+ for cloud video analysis a common pain point, or am I an outlier? 2. What other analyzers would be useful? (thinking about camera movement analysis and scene type classification like POV/vlog/interview) 3. Should I prioritize making it 100% offline by default, or is the Gemini API option fine for most use cases? GitHub: https://github.com/iliashad/edit-mind Demo: https://youtu.be/Ky9v85Mk6aY Built this over a few weekends because I was tired of paying Google to search my own videos. Happy to discuss architecture decisions or the ML pipeline!