What is the reafference loop?

It is a feedback mechanism that compares intended signals with observed results to determine if a signal change was self-generated or caused by the external environment.

How does the 'Spectral Gap 1/4' help?

It represents the mathematical 'sweet spot' for mixing time in GF(3) systems, balancing the AI's ability to explore new frequencies without losing track of current signal objectives.

What is the 'Borges' aspect of this skill?

It treats the radio spectrum as an infinite library of possible transmissions, allowing the AI to navigate frequency space like a librarian searching for specific data patterns.

Do I need hardware to use this?

While designed for Software Defined Radio hardware via GNU Radio, it can operate on simulated data and mathematical models within the Claude environment for testing.

What is GF(3) skill refinement?

It is a conservation principle where every refinement cycle generates exactly three new sub-skills: one for verification (Minus), one for coordination (Ergodic), and one for exploration (Plus).

Borges SDR Reafference Engine

Name: Borges SDR Reafference Engine
Author: plurigrid

byplurigrid

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数据科学与机器学习

Orchestrates self-learning signal processing and spectral exploration using software-defined radio (SDR) and categorical database integration.

关于

The Borges SDR Reafference Engine is a specialized signal processing framework that treats Software Defined Radio as an infinite information landscape, inspired by Jorge Luis Borges’ 'Library of Babel'. It enables Claude to perform autonomous spectral exploration using optimal mixing times (Spectral Gap 1/4) and maximally mixed states to maximize agency during discovery. By comparing intended transmissions (efference) with observed signals (reafference) via von Holst’s principle, the skill allows for self-correcting random walks across frequency space, integrated with ACSet categorical data structures for complex signal cataloging and automated triadic skill refinement.

主要功能

Optimal 1/4 spectral gap mixing for balanced exploration and exploitation
2 GitHub stars
Categorical signal management and indexing using ACSet schemas
GNU Radio integration for real-time and simulated SDR flowgraph control
Reafference-guided spectral random walks for autonomous signal discovery
GF(3)-based skill refinement for balanced validation and generation

使用场景

Management of massive signal libraries with categorical indexing
Autonomous spectrum scanning and anomalous signal detection
Adaptive modulation scheme design using self-learning feedback loops

关于

主要功能

Optimal 1/4 spectral gap mixing for balanced exploration and exploitation
2 GitHub stars
Categorical signal management and indexing using ACSet schemas
GNU Radio integration for real-time and simulated SDR flowgraph control
Reafference-guided spectral random walks for autonomous signal discovery
GF(3)-based skill refinement for balanced validation and generation

使用场景

Management of massive signal libraries with categorical indexing
Autonomous spectrum scanning and anomalous signal detection
Adaptive modulation scheme design using self-learning feedback loops