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 […]

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 […]

Professor Codephreak asked automindX to audit itself; its ten-point blueprint describes what mindX (the tree grown from automindX gitmind seed) already is. The mirror is honest: one gap (coverage 70 vs 80) is named, not hidden. Improvement is a circuit, not a line.