> For the complete documentation index, see [llms.txt](https://docs.marketcompass.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.marketcompass.ai/subnet/subnet-17.md).

# Subnet 17

Market Compass utilizes Commune AI, specifically Subnet 17, to gather real-time data from X (Twitter) at scale. Here's how the subnet operates:

1. **Query Creation:**
   * Queries are created to access the X (Twitter) API, targeting market-related topics.
2. **Query Distribution:**
   * Miners within Subnet 17 receive a unique query to perform every minute, just before each voting cycle begins.
3. **Query Validation:**
   * Miners return updates for all queries, ensuring that the data collected is fresh and relevant.
   * Trust is placed in miners by default, assuming they provide accurate and valid data.
4. **Backend Verification:**
   * An undisclosed endpoint in our backend verifies random queries with timestamps provided by miners.
   * If the response differs from the expected data, the miner is blacklisted, ensuring data integrity.
5. **New Miner Validation:**
   * New miners undergo validation checks upon their first request to ensure data accuracy and reliability.
6. **Scoring System:**
   * The scoring system rewards miners for providing valid data, with emission distributed evenly among valid miners.
   * This approach builds a resource base necessary for future development and expansion.


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# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.marketcompass.ai/subnet/subnet-17.md?ask=<question>&goal=<user_goal>
```

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`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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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.