Self Thinking Data

Self-Thinking Data is a new way to let documents and data guide how they are understood by Large Language Models. This makes documents interactive and self-directing. It keeps the author''s original meaning while adding new ways to analyze the data. Self-Thinking Data puts instructions inside documents, images, or data files. These instructions tell the Large Language Models how to process, analyze, and show the data. This makes smart artifacts that can create interactive interfaces, do data analysis, and give smart help based on the data. Benefits of Self-Thinking Data include interactive experiences, keeping the author''s intent, better analysis, smart help, and custom ways to explore documents. Self-Thinking Data can be used in many ways. For example, in academic research, data visualization, educational materials, technical documentation, and business reports. Self-Thinking Data is still new. While it shows a lot of promise, it is important to be careful. The community should think about security and follow the guidelines. People have seen the potential for more engaging documents but also know that careful use is needed to avoid problems. Self-Thinking Data is in an experimental phase. The v0.1 specification explains the idea. The project is open-source and on GitHub. It welcomes contributions and feedback from the community. The specification also includes ways to handle risks and makes sure the community is careful and thinks about security.