> For the complete documentation index, see [llms.txt](https://github-52.gitbook.io/t-mac-dao-white-paper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://github-52.gitbook.io/t-mac-dao-white-paper/ecosystem.md). # Ecosystem We will use the DAO as the core to aggregate valuable contributors. We will use eco-products as well as events as a grip to expand the global influence of basketball stars. Including three modules - 1, The T-Mac DAO. A fan communication space with incentives plus governance as the core module. 2, Eco-products. A content contribution system functions to motivate the community to obtain rewards through various forms of interaction. Ecological peripheral product development support: including NFTs, social, meta-games, interactive brands, etc. 3, Activities. Peripheral activities: including but not limited to online virtual communication, offline, fan gatherings, peripheral charity activities and other forms. Peripheral cooperation: including web3 metaverse events in the Sandbox, Decentraland, and cooperation with other well-known NFT brands and projects. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://github-52.gitbook.io/t-mac-dao-white-paper/ecosystem.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.