Okay, I saw a post. People were loading in their ChatGPT generated storyboards into Seedance 2.0 and it was animating their sequences. So I tried it. UMMMM This is amazing. Storyboards I used attached too. Here is the first pass of the animation. Incredible!

Thank you!

For professionals utilizing artificial intelligence to manage creative workflows, unstructured data is a critical liability. Advanced tools like Codex and Claude Code are exceptionally capable, but they cannot inherently deduce the context of a 50GB raw video file, a high-resolution design asset, or a complex project directory based solely on a filename. When files are deposited into a drive without structural context, it forces the AI to operate on assumptions, which leads to inaccurate outputs and wasted time. The solution is an architecture known in AI engineering as the LLM Wiki or Open Knowledge Format. By standardizing a system of lightweight, structured text files adjacent to your creative assets, you provide AI agents with a definitive, machine-readable roadmap of your entire archive. Here is the exact framework to implement this standard across your local and cloud drives. 1. The Directory Blueprint: _index.md Every primary folder requires an _index.md file. This document serves as the executive summary for the directory, establishing the project's core objectives, visual psychology intentions, and structural hierarchy. For example, a directory dedicated to an analog short film project would contain an _index.md that dictates the project timeline, the structural breakdown of the subfolders, and the foundational aesthetic principles the AI must adhere to when referencing that project. 2. The Asset Blueprint: The "Sidecar" Non-text files require immediate context. You achieve this by creating a Markdown file with the exact same name as the media asset, establishing a "sidecar" relationship. If you have a file named Scene_01_16mm_Scan.mov, you create a corresponding Scene_01_16mm_Scan.md in the exact same location. This sidecar file contains the specific metadata: shot lists, focal lengths, lighting diagrams, and narrative context. When the AI scans the directory, it reads the text sidecar to fully understand the contents and intent of the adjacent media file.

https://www.criterion.com/boxsets/9000-the-complete-kubrick