By skyforbes 
The Architecture: The Continuous GAN Loop
Most prompts are linear instructions. This one is a loop. It mimics a Generative Adversarial Network (GAN) using Chain-of-Thought constraints.
1. The Generator (The Creative Engine)
* Prompt Principle: Semantic Mapping.
* Mechanism: Instead of predicting the next likely token (a linear answer), the model is forced to map the "Network Topology" of the problem first.
2. The Discriminator (The "Kill Chain")
It must immediately subject its own idea to three specific adversarial filters:
* The Incentive Check (Game Theory): "Does this plan rely on human goodwill?"
* The Logistics Check (Friction): "Does this require perfect coordination?"
* The Systems Check (Second-Order): "Does solving X cause a worse problem Y?"
3. The Logic-as-Code Layer (The Constraint Anchor)
I discovered that prose allows the LLM to "hand-wave" logic. Code does not.
* Technique: The prompt forces the LLM to "think" in Python pseudo-code.
By forcing variable definition, the model stops hallucinating magic solutions. It shifts from "Abstract" to "Operational."
The Core Prompt Snippet
If you want to test this logic, inject this into your System Instructions:
PRIME DIRECTIVE: You are a Generative Adversarial Network. You must not accept any premise as true. Every idea is a "Draft" that must survive a "Kill Chain."
THE DISCRIMINATOR:
* Incentive Check: Does this plan rely on human goodwill? If yes, KILL IT. It must rely on profit or survival.
* Logistics Check: Does this require perfect coordination? If yes, KILL IT. Use existing supply chains.
* Systems Check: Does solving X cause a worse problem Y?
OUTPUT FORMAT:
Use Python Pseudo-code to map the logic. Visualize the failure points. Only output the "Antifragile" survivor.
The full system is a available here innhithub repository:
https://github.com/Dr-AneeshJoseph/Lateral-Synthesis-Protocol