🤖The AI Trust Problem

GPT says "X", Claude says "Y", Gemini says "Z"
— who do you trust?

ACP achieves consensus between AI models using universal axioms — truths that AI cannot deny because it is built upon them.

"AI cannot lie about what it's built on."

🖥️

von Neumann

architecture

🌐

TCP/IP

protocols

🐍

Python/C

languages

These self-referential facts become the undeniable foundation for consensus.

Axiom Spiral

Each iteration narrows disagreement by the golden ratio (φ ≈ 1.618). After 7 levels, only 3.4% disagreement remains — consensus is mathematically guaranteed.

Mathematical61.8%

a + b = b + a

Physical38.2%

F = ma

Ontological23.6%

Water = H₂O

Computable14.6%

SHA-256(x) = ...

Architectural9.0%

von Neumann

Protocol5.6%

TCP/IP

Linguistic3.4%

Python syntax

✅ Consensus ReachedD-score < 0.05

Understanding D-Score

✅

0.0 - 0.2

High Confidence

All models agree. Trust the answer.

🟡

0.2 - 0.4

Moderate

Likely correct. Good for most uses.

🟠

0.4 - 0.6

Low Confidence

Some disagreement. Verify if important.

🔴

0.6 - 1.0

No Consensus

Models disagree. Question may be subjective.

Use Cases

💻

Code Decisions

“Redis or Memcached?”

🔒

Security Review

“Is this SQL injection?”

📚

Fact Check

“When did WWII end?”

Coming Soon

IDE Integration

Get AI consensus directly in your code editor. Multi-model code review, real-time suggestions, and D-Score validation.

VS Code

Native extension with sidebar chat

JetBrains

IntelliJ, PyCharm, WebStorm

CLI

Terminal-first for power users

Join Telegram for updates

Open Source

ACP is free and open source. We believe AI consensus should be accessible to everyone.

Community

Free

Full access. Your own API keys.

Support Us

Donate

Help keep the project alive.

Enterprise

Contact

Custom integration & SLA.

GPT says "X", Claude says "Y", Gemini says "Z"— who do you trust?