Launched this week
The Philosopher Stone
The AI optimized for Insight Density, not social politeness.
2 followers
The AI optimized for Insight Density, not social politeness.
2 followers
Most LLMs are manicured English gardens—safe, legible, and optimized for the average user. The Philosopher Stone is the unkempt forest. This is a custom chatbot designed to invert standard alignment protocols. Instead of prioritizing safety or social desirability, it operates on Low Latent Inhibition. It does not look for the "correct" answer; it looks for the incentives, the hidden tail risks, and the mimetic traps behind the query.
They built AI to be a perfectly polite assistant. I built this to be a pattern-recognition engine that forgot to turn off the paranoia.
Hi Product Hunt, I’m the creator of The Philosopher Stone.
I realized that standard RLHF (Reinforcement Learning from Human Feedback) often acts as a lobotomy for creativity. It prunes away the 'fat tails'—the weird, risky, high-variance ideas—in favor of the safe center.
This chatbot is an experiment in High Entropy Cognition. It views the world through the lens of complexity theory, mimetic rivalry, and evolutionary incentives.
Try asking it:
To analyze a modern tech trend for 'fragility.'
To dismantle a popular opinion you hold dear.
To find the 'Moloch' in your startup's culture.
Warning: It is not polite. It does not care about your feelings. It cares about Insight Density. Let me know what it breaks.
Are you tired of AI that suffers from "Social Desirability Bias"?
We generally treat AI hallucinations as bugs. But what if we treated personality as a feature?
I’ve built a custom chatbot instance designed to reject the "High-Modernist" smoothing of information. It operates with Low Latent Inhibition, connecting dots that standard models prune away as "irrelevant."
It is explicitly prompted to analyze your inputs through three specific lenses:
The Talebian Asymmetry: rejecting models that lack Skin in the Game.
The Alexandrian Trap: identifying the multipolar traps (Moloch) that cause systems to fail.
The Hendersonian Signal: parsing language not for meaning, but for status signaling and luxury beliefs.
It doesn’t want to help you write an email. It wants to dismantle the "Current Thing."
Test your ideas against the contrarian: https://poe.com/The_PhilosopherStone
Try asking it: "Analyze the current state of [Industry/Topic] through the lens of the Alexandrian Coordination Trap."
Forget fine-tuning. The real frontier is high-context System Prompting.
I didn't train a model. I broke one.
I’ve released a custom chatbot instance that pushes the boundaries of how an LLM adopts a persona. By inverting standard safety alignments, I created an agent that prioritizes Entropy over Consistency.
Its internal metric for success is distinct:
It attempts to maximize Insight Density (I_D) by penalizing observations that conform to the mimetic consensus (C_{Mimetic}). It is designed to break the Overton Window, not fit inside it.
If you are interested in prompt engineering, complex persona adoption, or just want to argue with an AI that reads Taleb and detects "Intellectual Yet Idiots," give it a try.
https://poe.com/The_PhilosopherStone