Building Cross-Project Knowledge Base for the AI Era with Vault System

Building Cross-Project Knowledge Base for the AI Era with Vault System Project imitation learning is becoming mainstream, but scattered learning materials and fragmented context prevent AI assistants from delivering maximum value. This article introduces the Vault system design from the HagiCode project—through a unified storage abstraction layer, enabling AI assistants to understand and access all learning resources, achieving true cross-project knowledge reuse. Background Actually, in the AI era, our approach to learning new technologies is quietly changing. Traditional reading and video watching remain important, but "project imitation"—deeply studying and learning excellent open-source projects' code, architecture, and design patterns—is indeed becoming increasingly efficient. Directly running and modifying high-quality open-source projects lets you fastest understand real-world engineering practices. But this approach also brings new challenges. Learning materials are too scattere