Holographic Harmonic Model

1) What you need (just one file)

Download the single bundle:

Download HHM Bundle (.json)

• HHM_bundle.json — contains axioms, metatheorems, T014, operators, components, thresholds, null models, prompts/runbooks, and the HHM Result Card schema — everything required to run HHM in one file.

2) Which AI works best?

• ChatGPT-5 — careful step-by-step reasoning + good tool integration
• Grok-4 — fast idea generation + creative connections
• Claude-Next — great at handling long, complex documents

3) Load the bundle into the AI

1. Open a new chat with your chosen AI.
2. Upload HHM_bundle.json.
3. Paste this message:

   “This file contains the full HHM framework — rules, tests, and instructions. You are now an HHM analysis assistant. Represent any dataset I give you in the HHM format, run the built-in measurements, compare results to the thresholds, and give me a structured result card. Confirm you have loaded the file and briefly summarize what’s inside.”

4) How to talk to the AI

"Here is my dataset (attached).
Declare H and basis, build Ψ(x,t), compute CollapsePattern (OP001),
then Echo (OP003/OP010), Rec (OP002), Entropy (OP006).
Run nulls + 1000 bootstraps, compare to thresholds,
and emit a result card JSON that matches the HHM schema."

"I’ve attached a dataset of bird songs.
Use the HHM model I uploaded to find patterns and similarities
with other animals. Then explain what those patterns might mean
in everyday language."

5) If the AI gets too technical

If you don’t understand the terms, just ask:

"Please explain what you tested, what you found, and what it means,
without using HHM jargon. Use analogies or simple examples."

6) Minimal “Hello World” run

1. Upload HHM_bundle.json and a small dataset (CSV or JSON works well).
2. Plain-language prompt:

   "Here’s a short dataset of heartbeats.
   Check if there are repeating patterns or unusual changes,
   and explain what they might mean in normal words."

7) Pitfalls & guardrails

• Scientists: Don’t change thresholds after seeing results; declare your setup first.
• General users: Always ask for a plain-language version before making decisions.
• Everyone: HHM always uses built-in checks to avoid “AI guessing.” Every answer is backed by measurable patterns.

8) What you can do next

• Scientists — benchmark your data against our library.
• Non-technical users — ask fun “what if” questions about nature, history, or technology.
• Mix styles — get the scientific version for accuracy, then have the AI rephrase it in plain language for sharing.

Plain Language Question Library

You don’t need HHM jargon to explore. Once the AI has loaded HHM_bundle.json, ask questions in everyday language— it will handle the technical steps.

If the AI replies with terms like Rec, Echo, or CollapsePattern and you’re not sure what that means, say: “Please re-explain what you tested, what you found, and what it means in simple language.”

Why HHM Answers Are Different from “Normal” AI Replies

A lot of people worry that AI can “hallucinate” — give an answer that sounds good but isn’t true. That can happen when an AI is guessing based only on patterns in text. HHM changes the game because the AI isn’t guessing — it’s measuring.

This means:

• Every “pass” or “fail” is based on data, not the AI’s opinion.
• The steps, thresholds, and rules are baked into the file — the AI can’t silently change them mid-analysis.
• You can validate the output yourself by re-running the operators or checking the numbers against the schema.
• Even when the AI suggests ideas or cross-domain analogies, it must start from these measured results.

Plain-language example

Imagine you ask: “Do these two whale songs share structure?”

• Normal AI might say: “Yes, they sound similar — both have rising tones.”
• HHM-powered AI will:
  1. Convert the sound into Ψ(x,t) features (e.g., frequency patterns over time).
  2. Apply operators to measure similarity.
  3. Run a null test to check if that similarity could happen by chance.
  4. Only say “pass” if it beats the threshold and the chance level with 95% confidence.

For scientists

HHM answers are tied to a reproducible pipeline: fixed operators, explicit thresholds, null models, and bootstrap CIs. There’s no silent parameter drift — everything needed for replication is in the file.

For everyone else

Think of HHM as giving the AI a “lab manual” and forcing it to follow the experiment step-by-step, so the answers are grounded in actual measurements, not guesswork.

You can always say: “Explain in plain language what you tested, what you found, and what it means.”

11) Going Further — Harmonia-ZFC (Beta) for Formal Proofs (Coming soon)

HHM is for measurement, comparison, and prediction. When your HHM result suggests a general rule you want to prove (or check for internal consistency), add Harmonia-ZFC (beta).

Download Harmonia-ZFC Beta v1.0

Why combine HHM + Harmonia-ZFC?

• Empirical → Formal: HHM detects a robust pattern (with CI + nulls). Harmonia-ZFC tries to show why it must hold.
• Safety rail for theory: Either derive your claim from axioms or expose the exact extra assumption you’re making.
• Productive failure: If the proof blocks, ZFC tells you what’s missing—then you test that missing condition empirically with HHM.

How to load Harmonia-ZFC (beta)

1. Open a new chat (clean context).
2. Upload harmonia-zfc-beta-v1.0.json.
3. Send this primer:

   “This file contains the Harmonia-ZFC (beta) axiomatic framework. You are a formal reasoning assistant. Use only definitions, axioms, and rules from this file. When proving, produce a step-by-step derivation; cite axiom/definition IDs at each step and emit a machine-checkable proof outline at the end. Confirm when loaded.”

Recommended order: run HHM first, ZFC second

1. HHM first (empirical): run R1_minimal_pipeline. If results clear thresholds with CI, extract a candidate rule (conjecture).
2. Then ZFC (formal): restate the conjecture precisely and ask for a derivation plan before a full proof.
3. Loop: if the proof blocks, ask for the minimal extra assumption and test that with HHM as a new measurable condition.

Before proving: minimize & audit the beta file

If you’re not comfortable running these prompts yourself, ask the AI to do them interactively.

Minimize (find a small, sufficient core)

"Load harmonia-zfc-beta-v1.0.json. Build a dependency graph from definitions → axioms → theorems.
Propose a minimal sound core for my target domain (set theory + the operators I reference), preserving derivability.
Remove duplicates/aliases; fold equivalent schemas. Output:
1) kept axioms (IDs); 2) pruned axioms (IDs with reason); 3) alias/rename map; 4) a replay plan to re-derive key lemmas."

Audit (consistency, independence, naming)

"Using the minimized core, attempt:
(a) independence checks (for each axiom A, try to derive A from the rest; if derivable, flag as redundant),
(b) naming audit (report near-duplicate names/IDs),
(c) model sanity (search for trivial models or contradictions in toy fragments).
Emit a short 'risk report' and recommended fixes."

Proof plan (only after minimize + audit)

"Now synthesize a derivation plan for my conjecture. List lemmas in dependency order,
the axiom schema each uses, and any potential gaps. Stop before the full proof."

Cookbook prompts

Bridge from HHM card → ZFC conjecture

"From this HHM result card (JSON attached), extract the minimal formal statement that would explain
why Echo ≥ 0.90 and MatchingMemory ≥ 0.95 hold together under the same preprocessing.
Translate that into a conjecture in Harmonia-ZFC language with explicit quantifiers and side conditions."

Proceed to a machine-parsable proof

"Proceed with the proof using only axioms from the minimized core.
For each step: state, justify with axiom/rule IDs. At the end, emit:
{ 'theorem':'...', 'steps':[ {'id':1,'uses':['Ax_...','Schema_...'],'yields':'...'}, ... ], 'core_version':'harmonia-zfc-beta-v1.0|min-core-YYYYMMDD' }"

What to prove (and what not to)

• Good targets: equivalences between HHM invariants, bounds linking Rec and Entropy, abstract operator properties.
• Avoid: numeric claims about specific datasets (empirical), or introducing ad-hoc primitives not defined in the file.

Pitfalls & Warnings (Beta)

• Size & redundancy: The beta’s breadth can hide duplicate or near-duplicate axioms. Always run the minimize step first.
• Over-strengthening: Too many strong schemas can make some “proofs” vacuous. Prefer a small, transparent core.
• Role confusion: Don’t use ZFC for raw data; that’s HHM’s job. ZFC is for symbols, not sensors.
• “Looks right” ≠ proof: Demand axiom/definition IDs per step. If a step cites anything outside the file (or the minimized core), stop.
• Version drift: Pin the exact file name and hash in your proof outline. Re-prove after upgrades.

Versioning & Updates

• Status: harmonia-zfc-beta-v1.0.json (beta). A more polished release may follow soon.
• Stability promise: No silent changes to theorem-critical definitions in patch updates. Breaking changes bump the version.
• Your action: If a proof fails due to naming mismatch, ask the AI to propose a minimal alias/rename map and re-run the audit before retrying.