From: aidotengineer
Foundational Concepts
In the field of Natural Language Processing (NLP), a legendary paper titled “Attention Is All You Need” is widely recognized [00:00:39].
AI Agents in Video Editing
An open-source video editing agent has been developed to automate video editing for a platform called ReSkill, which focuses on personalized learning [00:01:12], [00:01:17]. Initial attempts with FFmpeg showed limitations, leading to the search for more intuitive and flexible alternatives [00:01:22]. Remotion was considered but had unreliable server-side rendering [00:01:30].
The “Core” library from Diffusion Studio was chosen due to its API, which did not require a separate rendering backend [00:01:35]. This library enables complex compositions via a JavaScript/TypeScript-based programmatic interface [00:01:49]. This allows Large Language Models (LLMs) to generate code to run the system [00:01:54].
LLMs and Code Generation
When an LLM can write its own actions in code, it forms a perfect match because code is considered the best way to express computer actions [00:02:00], [00:02:08]. Multiple research papers have indicated that LLM tool calling implemented in code is significantly more effective than in JSON [00:02:16].
Agent Architecture and Workflow
The current architecture of the video editing agent operates as follows:
- Browser Session The agent initiates a browser session using Playwright and connects to an Operator UI [00:02:27], [00:02:30].
- Operator UI This web application serves as a video editing interface specifically designed for AI agents [00:02:35]. It renders video directly in the browser using the Web Codecs API [00:02:42].
- File Transfer Helper functions facilitate file transfer between Python and the browser via the Chromium DevTools protocol [00:02:46].
The typical workflow involves three main tools:
- Video Editing Tool Generates code based on user prompts and executes it in the browser [00:03:08].
- Doc Search Tool Utilizes Retrieval-Augmented Generation (RAG) to fetch relevant information if additional context is needed [00:03:14].
- Visual Feedback Tool After each execution step, video compositions are sampled (currently at one frame per second) and fed to this tool [00:03:25]. The visual feedback tool acts as a generator and discriminator, similar to the Generative Adversarial Network (GAN) architecture [00:03:33]. Once the visual feedback tool gives a “green light,” the agent proceeds to render the final composition [00:03:42].
Agent Control and Deployment
A file named lm.txt has been shipped, which functions similarly to robots.txt but for agents [00:03:51]. lm.txt, combined with specific template prompts, can significantly aid in the video editing process [00:04:00].
The current setup is flexible, allowing the agent to connect to a remote browser session via WebSocket, enabling each agent to have a separate, GPU-accelerated browser session behind a load balancer [00:04:14].
The first version of the agent is implemented in Python, with a TypeScript implementation currently underway [00:04:37]. This aligns with the saying that “any application that can be written in TypeScript will be written in TypeScript” [00:04:49]. This project is a collaboration between Diffusion Studio and Rskill [00:04:56].