MASTERMIND

MASTERMIND

MASTERMIND documentation on the blockchain

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aGLM

aGLM, or Autonomous General Learning Model, is designed to operate as a core model for autonomous data parsing and learning from memory in the context of artificial intelligence systems. It’s a pivotal element within a broader system called RAGE (Retrieval Augmented Generative Engine). Key aspects and functionalities of aGLM: Autonomous Learning: aGLM is built to learn autonomously from interactions and data retrievals. It continuously updates its knowledge base, refining its capabilities based on new data […]

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Abstract flow-state composition — chosen as the featured image for the Quantum Machine Learning Code Compendium 2026: a research-mastery visual for a reference and recovery atlas of QML code in the year before fault tolerance.

A canonical compendium of quantum machine learning code, in the year before fault tolerance

A canonical compendium of quantum machine learning code in the year before fault tolerance. Framework-agnostic, organized as both reference and recovery atlas — preserving the early code of QML (Wittek’s MOOC, Rigetti’s Grove, Zapata, Microsoft LIQUi|⟩, qiskit-aqua) before it vanishes. PDF mirror included.

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ezAGI

ezAGI

Augmented Generative Intelligence Framework The ezAGI project is an advanced augmented generative intelligence system that combining various components to create a robust, flexible, and extensible framework for reasoning, decision-making, self-healing, and multi-model interaction. Core Components MASTERMIND Purpose:The mastermind module serves as the core orchestrator for the easyAGI system. It manages agent lifecycles, integrates various components, and ensures the overall health and performance of the system. Key Features: SimpleCoder Purpose:The SimpleCoder module defines a coding agent […]

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