> For the complete documentation index, see [llms.txt](https://help.zke.com/symbol/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.zke.com/symbol/badger-badger-dao.md).

# BADGER(Badger DAO)

* Issue Time

  \--
* Total Supply

  21,000,000 BADGER
* Circulation

  18,868,747 BADGER
* White paper

  \--
* X Twitter

  <https://twitter.com/badgerdao>
* Telegram

  <https://t.me/badger\\_dao>
* Website

  <https://app.badger.finance/>
* Block Explorer

  <https://etherscan.io/token/0x3472a5a71965499acd81997a54bba8d852c6e53d>

Badger is a decentralized autonomous organization (DAO) with a single purpose: build the products and infrastructure necessary to accelerate Bitcoin as collateral across other blockchains. Badger DAO aims to create an ecosystem of DeFi products with the ultimate goal of bringing Bitcoin into Ethereum. It is the first DeFi project that chose to focus on BTC as the main reserve asset rather than using ETH. During launch, there are two main products, Sett and DIGG. They believe that together the community can build the products that our industry needs more effectively, compared to single centralized entities building fragmented solutions.

{% hint style="info" %}
Trade on ZKE Exchange：<https://www.zke.com/&#x20>;

Twitter：<https://twitter.com/ZKE\\_com&#x20>;

Telegram：<https://t.me/ZKEGlobal>

Support: <https://support.zke.com>
{% endhint %}


---

# 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:

```
GET https://help.zke.com/symbol/badger-badger-dao.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.