Menu

Professor Codephreak

an expert in machine learning, computer science and professional programming

chmod +x automindx.install && sudo ./automindx.install

is working. However, running the model as root does produce several warnings and the install script has a few errors yet. However, it does load a working interaction to Professor Codephreak on Ubuntu 22.04LTS

So codephreak is.. and automindx.install is the installer with automind.py interacting with aglm.py and memory.py as version 1 point of departure. From here model work continues on fixing the installer to more sanity, and including the components of MASTERMIND rationality to create logic and prediction. Following MASTERMIND integration adding the RAGE components and putting memory into a vector database seems logical. Am exploring llamaindex a bit more. Work continues. I found pipx today, and this is likely more sane than miniconda3. Also looking at venv to control the environment variables for multi-model scenarios.

Interaction with Professor Codephreak GPT4 Platform Architect and Software Engineer available at

Professor Codephreak Software Engineer Machine Learning Platform Architect

codephreak.openai.agent

Related articles

SimpleMind

blueprint for a SimpleMind Using easyAGI

Abstract: This article conceptualizes the creation of an advanced Autonomous General Intelligence (AGI) system, named “easyAGI,” integrating several cutting-edge AI components. Theoretical in nature, this blueprint outlines the essential modules required to construct such a system, emphasizing the principles behind each component without delving into implementation specifics. Introduction: The pursuit of AGI aims to create a machine capable of understanding, learning, and performing intellectual tasks across various domains, akin to human cognitive abilities. The easyAGI […]

Learn More
AGInt: the cognitive engine at the heart of mindX — Perception, Orientation, Decision, Action, with RAGE for memory

AGInt: the cognitive engine at the heart of mindX — Perception, Orientation, Decision, Action, with RAGE for memory

Why /core matters, how the P-O-D-A loop connects beliefs to behavior, and the four-tier RAGE memory cascade (STM → LTM → pgvector → IPFS) that keeps the deliberation layer grounded.

Learn More
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 […]

Learn More
Close Menu