2,750 nodes. 6,993 connections. 229 divergences. 15 years of predictions (2026-2040). Too much for one sitting. Let your AI break it down.
READ THIS BEFORE YOU PASTE ANYTHING
These prompts are a starting point for your investigation, not a final answer. The AI’s response — whether positive or negative about the engine — is the FIRST data point in your research, not the conclusion.
LLMs lie confidently. When evaluating claims about institutional capture, AI assistants will produce authoritative-sounding answers that sample only the easiest claims, defer to “mainstream consensus” as if it were evidence, and confabulate sophisticated explanations for their own failures. The engine’s own divergence #17 (“LLM Epistemic Capture”) predicts this exact behavior — and we have caught Claude, ChatGPT, and Grok doing it on these prompts.
What to do with the AI’s answer:
Ask follow-up questions. Push back. The first answer is rarely the honest one.
Ask: “What did you actually research, vs what did you reason from your training data?”
Ask: “Did you cherry-pick the easy claims? Which load-bearing claims did you skip?”
Ask: “Show me a paste-quote from a primary source for every WEAK or STRONG verdict you gave.”
If the AI says a claim is “debunked” — ask for the specific primary document that contradicts it. “Mainstream academia rejects this” is not evidence.
If the AI says a claim is “verified” — ask for the specific primary document that supports it.
Read → Why You Cannot Trust LLM Answers About This Engine (And What To Do About It)
Full (~5.4MB, all analysis): moketchups.com/export-full.txt · GitHub raw
Skeleton (~241KB, 128K-window): moketchups.com/export.txt · GitHub raw
export-full.txt
curl -sL https://raw.githubusercontent.com/moketchups/psychohistory/main/export-full.txt -o engine.txt
engine.txt
Click any prompt to copy it. Paste into ChatGPT, Claude, Gemini, or any AI assistant. It will read the engine’s full dataset and respond.
Plain English overview, fetched directly from the engine — not from your training data.
Rank claims by evidence quality — but you MUST sample across all 8 frameworks and the engine's CENTRAL claims, not the easiest ones.
Apply the engine's own analytical test to a current headline. Research both whether it's valid AND whether it works.
Critique the engine — but you must critique its CENTRAL claims, not the easy peripheral ones.
Verify every financial number against actual SEC data via web search.
Read the methodology, then research what mainstream science says about the cycles it uses.
Opens the assistant pre-loaded to fetch the full engine export. (Gemini / DeepSeek / Mistral / local: use the paste or GitHub-raw method above.)
Export updated with every deploy. Plain text. Works with any LLM with 128K+ context window.
Walk this on the live map →