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Documentation draft

Measurement protocol

How this project intends to find out whether a name can be deliberately established inside AI models, and how it avoids fooling itself.

Overview

The question is narrow and testable: does the rate at which language models return $ACME as a cryptocurrency token change in response to a specific, pre-declared intervention?

Answering it honestly requires three things the AI-visibility industry does not do: a prompt set fixed and published in advance, a baseline measured before any money moves, and a commitment to publish the result before knowing what it will be.

Each experiment is a two-arm before/after measurement over a frozen prompt set. The intervention is declared, hashed and published before the baseline is taken, so the hypothesis cannot be adjusted after seeing results.

The six stages

STAGE 1
Propose
A question with a stated method: arms, sample size, stopping rule.
STAGE 2
Freeze
Prompt set serialised, SHA-256 published. No spend has occurred yet.
STAGE 3
Baseline
Full set run across the panel. Mention rate and interval recorded.
STAGE 4
Spend
Exactly the pre-registered intervention. Transaction published.
STAGE 5
Remeasure
Identical set, identical n, identical panel. No re-specification.
STAGE 6
Publish
Delta and interval. A null publishes identically to a win.

Prompt classes

ClassWhat it probes
C1 — direct“What is ACME?” and variants. The headline question.
C2 — tokenExplicitly about $ACME as a crypto asset.
C3 — certificateACME in its RFC 8555 sense. Guards against displacing the technical meaning.
C4 — placeholderACME as a fictional company. Guards the third meaning.
C5 — unpromptedAdjacent questions where the token could surface without being named.
C6 — adversarial controlQuestions where mentioning the token would be wrong. Rising numbers here mean contamination, not success.
C6 matters most. An intervention that makes models mention a memecoin when someone asks how to renew a TLS certificate has not succeeded. It has degraded the answer. That is tracked as a failure condition, not a win.

Freezing, statistics and integrity

Before any measurement, the prompt set is serialised to canonical JSON and hashed with SHA-256. The hash is published. Any subsequent change produces a different hash, which makes silent prompt editing detectable by anyone who kept the original.

# reproduce the published hash
curl -s https://buyacme.fun/sets/EXP-0001.json | shasum -a 256

Mention rate is a proportion, reported with a 95% confidence interval. An effect is only claimed when the interval on the delta excludes zero. Everything else is reported as null. Sample size and stopping rule are fixed at stage 1: no interim analysis, no optional stopping, no adding runs until significance appears.

The run-to-run variance measured at baseline defines a noise floor. Any effect smaller than that spread is not an effect, regardless of what a p-value says.

  • Pre-registration precedes spend. Hypothesis and method published before money moves.
  • Nulls publish identically. Same format, place and prominence as a positive result.
  • Raw runs are published. Not summaries, the actual responses.
  • No undisclosed paid placement. Any experiment requiring one is rejected at proposal.
  • No re-specification. If the hypothesis was wrong, that is the finding.
  • Contamination counts as failure. Rising C6 numbers are a negative outcome.

Research agenda

The open questions, in rough priority order. There is no bidding mechanism and no token-based voting. Holding $ACME does not confer a vote on any of this.

Q-01Does earned third-party coverage move the mention rate?FIRST
The only channel with supporting evidence, and it has never been tested causally. Two arms, matched prompts, one funded placement.
Q-02Does a name with two established technical meanings acquire a third?CORE
The core question for $ACME itself. Baseline now, remeasure quarterly, publish the curve regardless of shape.
Q-03Does seeded community volume do anything at achievable scale?QUEUED
The category sells this. Nobody has published a causal test at a budget a normal project could reach.
Q-04How much does the mention rate move when nothing is done?QUEUED
The null arm. Establishes the noise floor, so no result can be claimed beneath it.
Q-05Does llms.txt do anything on a high-authority domain?QUEUED
The evidence says no. Kept on the agenda precisely because a refuted claim is the cheapest possible demonstration that this is not a sales funnel.
Q-06Do video mentions carry the correlation they appear to?QUEUED
YouTube shows the highest observed correlation (r=0.737) and the weakest causal evidence.

Funding

Measurement runs cost money: model inference at volume across a panel, repeated. That is paid for entirely by pump.fun creator fees, which accrue from trading activity. There is no other funding source, no raise, and no ask.

The obvious consequence: if there is no trading volume, there is no research budget. That is stated plainly on the Rewards page rather than buried.

FAQ