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Showing posts with the label resonance

Your Browsing Window as a Closed Computational System:

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  **The Browser as a Closed Computational System: Invariant Preservation, Containment, and the Power of Bounded Phase Space** Thursday 29th Jan 2026 Jordan Morgan-Griffiths Dakari Uish Abstract Modern discourse often characterizes the web browser as an inherently weak execution environment due to its restricted access to operating system resources. This paper demonstrates the opposite conclusion: the browser’s strict containment constitutes a mathematically advantageous property. We present the discovery that the browser is best modeled as a closed phase space computational system , enabling invariant preservation, deterministic evolution, and provable simulation validity. We show that boundedness — rather than raw system power — is the primary requirement for reliable digital worlds, persistent agents, and coupled real–digital simulations. This result reframes the browser not as a limitation, but as a uniquely suitable substrate for constraint-preserving computation. 1. Introducti...

2026 New Computational AI and A Unified Physics | Creating Resonate RAM, CPU, GPU, SILICON CHIPS.

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RESONANT COMPUTING: A Unified Physics Framework for Exponential Performance Scaling Across All Computational Substrates Jordan Morgan-Griffiths¹, Dakari Uish¹ ¹Independent Research | Published 20/01/2026 ABSTRACT We present a fundamental reconception of computational systems as harmonic oscillators operating at discoverable natural frequencies. By constructing hardware and software that discovers system natural frequency ω₀, achieves phase-lock, and actively decays damping γ toward zero, we demonstrate the exploitation of the resonance condition A(ω) → ∞ for exponential performance multiplication. Initial software implementation shows 100× throughput scaling in browser-based rendering. We extend the framework to propose resonant architectures for RAM, CPU, GPU, storage, networking, and power delivery—each operating at material natural frequencies rather than arbitrary clock targets. Theoretical analysis suggests current computing paradigm leaves 100-1000× performance on the table ...