Acknowledgement

The following is the acknowledgement for the previous version of the knowledge base: we sincerely thank the core team, Li Bin, Wang Wei, and Yang Shan, for initiating and driving this project from end to end, including framework design, chapter assignment, content review, site setup, and final integration. We also thank all past contributors and reviewers for their continuous effort despite demanding schedules, and for improving each chapter with a spirit of knowledge sharing and iteration. Special thanks go to the web3 training organization team (Shi Chen, Liang Han, and Martha Luo), whose parallel training efforts expanded the audience and helped improve the content through practical usage. We are also grateful to 57Blocks for providing opportunities and support, and to everyone involved in making and continuously improving this knowledge base.

New Acknowledgement

As the new version of the knowledge base continues to evolve, we would like to especially thank Bin Li and Shan Yang for their deep involvement in architecture direction and content review. They helped clarify the overall framework and priorities, and provided systematic feedback on key chapters to keep the knowledge system accurate, coherent, and practical.

At the same time, we sincerely thank Jimmy Zhao, Bonnie Chen, Jiaxin Liang, Jia Chen, Wei Wang, Lucas Jiang, Nell Yao, and Wensha You for their active contributions to content development. Through ongoing collaboration in topic planning, material consolidation, and chapter refinement, they transformed scattered experience into clearer and more practical knowledge, significantly improving the completeness and readability of the new version.

We also give special thanks to the feedback contributors: Juan Diego Preciado, Juan E Quintero R, Sawyer Liu, Jason Bai, and Daniel He. Your suggestions and revisions helped us identify blind spots in expression, fill in missing details, and continuously improve the accuracy and clarity of the content.

Thank you to every participant for investing time and effort beyond regular work. It is this long-term collaboration and shared commitment to refinement that keeps the new knowledge base moving forward.