A configuration framework for AI agents grounded in RPCS-1 receiver dynamics. Translate environmental entropy, stakes, and predictability into specific LLM parameters.
I built RPCS-1 to solve a problem I kept running into: people often use the same words while processing reality through very different internal receivers.
Instead of labeling people by personality type or pathology, RPCS-1 models how someone receives, filters, updates, and resolves information.
The five core primitives are Temporal Integration, Signal Gain, Filtering Threshold, Update Elasticity, and Ambiguity Resolution.
My hope is that RPCS-1 becomes a shared language for communication friction, neurodivergence, work fit, relationships, and AI-human interaction.
This is an early launch, and I’m looking for feedback, critique, and collaborators who care about precision without dehumanizing people.
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