With the large-scale popularity of large models, we see the creative subject gradually shifting from humans to algorithms: code is automatically generated by AI, application templates can be combined in real-time, model parameters are continuously iterated, and Prompt engineering has even evolved into a new "software design language." In such a system, code is becoming the intelligent asset of AI systems, determining the core quality of model behavior, application logic, and content output.

However, in stark contrast to this trend, is the lag of underlying infrastructure. Over 100 million developers worldwide still rely on GitHub as their primary code hosting platform, while GitHub was originally designed as a collaboration tool, not an asset system. This brings a series of structural limitations that cannot be bypassed: repository ownership is controlled by the platform; content may be taken down or frozen for policy, geopolitical, or censorship reasons (such as the sanctions against many developers' repositories in 2022); development records cannot be verified on-chain, code lacks independent ownership proof, and cannot be traded, authorized, or valued like on-chain assets.

In an era where AI dominates content production, these issues are no longer just bottlenecks in tool experience, but directly determine whether content can truly become an asset. Nowadays, a data processing pipeline, a Prompt module, a piece of plugin logic, or the result of a model fine-tuning can all potentially become components of content with extremely high value density. However, in the centralized environment of Web2, these achievements are difficult to secure rights to, cannot circulate, and cannot be incorporated into an open economy.