Professor Codephreak

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Professor Codephreak Software Engineer Machine Learning Platform Architect

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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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Symbolic Logic

LogicTables Class: Managing Logic and Beliefs

The LogicTables class in logic.py is designed to handle logical expressions, evaluate their truth values, and manage beliefs as valid truths. It integrates with the SimpleMInd or similar neural network system to process and use truths effectively. Key Features: Initialization and Logging The LogicTables class initializes with logging configuration to capture debug information: Adding Variables and Expressions Truth tables are generated to evaluate logical expressions: Expressions are evaluated using logical operators: def evaluate_expression(self, expr, values):allowed_operators […]

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Autonomous Generative Intelligence Framework

Autonomous General Intelligence (AGI) framework

As we celebrate the establishment of the easy Autonomous General Intelligence (AGI) framework, it’s essential to appreciate the intricate steps that transform a user’s input into a well-reasoned response. This article provides a verbose detailing of this entire workflow, highlighting each component’s role and interaction. Let’s delve into the journey from user input to the final output. Stage one is nearly complete. reasoning from logic. 1000 versions later. This is the basic framework so far. […]

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