> For the complete documentation index, see [llms.txt](https://dopameme.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dopameme.gitbook.io/docs/start-here/core-idea.md).

# How Dopameme Thinks

Dopameme is built around a simple idea: meme-market edge comes from context, not raw alerts.

Most market tools show a token, a chart, and a feed of numbers. Dopameme tries to answer a deeper question: why should this token matter right now?

To do that, the terminal watches tracked-wallet behavior, scans token structure, records evidence into packets, and compares new observations against earlier reads. Over time, this creates a memory layer instead of a one-off alert feed.

Dopameme is built for traders who want to understand the market before reacting to it. A token moving is not enough. A wallet buying is not enough. A level appearing on a chart is not enough. The useful read comes from how those pieces fit together.

## The Main Loop

1. A token is scanned or observed.
2. Dopameme captures the relevant evidence.
3. The operator read explains what matters now.
4. Future scans compare against the earlier packet.
5. Confluence surfaces tokens where tracked-wallet attention passes quality filters.

This loop is what makes Dopameme more than a token list. It is designed to remember, compare, and improve the read over time.

## The Intelligence Stack

Dopameme combines several layers:

* Tracked-wallet behavior shows where known wallets are paying attention.
* Market-quality filters remove the worst low-cap and pre-bond noise.
* Token scans create the current market and chart context.
* Holder context helps show whether supply is concentrated, healthier, or risky.
* Operator reads translate evidence into plain-English consequence maps.
* Packets preserve the read so future scans have memory.
* Audits challenge the packet and feed learning back into the protocol.

The output users see should feel simple, but the read is built from multiple evidence layers.

## What Makes a Signal Useful

A signal is useful when it has context.

Dopameme cares about who acted, what the token looked like when they acted, whether the token has enough market structure to inspect, and whether the current read agrees with or contradicts past evidence.

That is why the terminal can ignore a large amount of raw activity. Not every tracked-wallet buy deserves to be shown to users.

The goal is to separate useful attention from noise. A token can be active and still be too small, too thin, too early, or too chaotic to deserve a user-facing read. Dopameme should surface the situations where attention and structure line up well enough to investigate.

## Why This Compounds

The protocol becomes more valuable as it observes more.

Every scan can teach the system something. Every packet creates a comparison point. Every audit can expose whether the previous read was strong, weak, or missing context. Over time, that means Dopameme should become better at showing which wallet behavior matters, which filters protect users, and which token structures deserve attention.

That is the long-term edge: not just seeing the market, but building memory around what the market keeps proving.


---

# 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://dopameme.gitbook.io/docs/start-here/core-idea.md?ask=<question>&goal=<endgoal>
```

`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.