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

Chain of TRUST in LLM

https://galadriel.com/ In the realm of artificial intelligence, verifying that an AI response genuinely came from a specific model and wasn’t tampered with presents a significant challenge. The Chain of Trust in verified AI inference provides a robust solution through multiple layers of security and cryptographic proof. The Foundation: Trusted Execution Environment (TEE) At the core of verified inference lies the Trusted Execution Environment (TEE), specifically AWS Nitro Enclaves. This hardware-isolated environment provides a critical security […]

Learn More

MASTERMIND aGLM with RAGE

Building a rational Autonomous General Learning Model with Retrieval Augmented Generative Engine to create a dynamic learning loop with machine.dreaming for machine.learning as a self-healing architecture. MASTERMIND uses the Autonomous General Learning Model (aGLM) enhanced by the Retrieval Augmented Generative Engine (RAGE) to create a sophisticated AI system capable of intelligent decision-making and dynamic adaptation to real-time data. This combination leverages the strengths of both components to ensure that responses are not only based on […]

Learn More

RAGE for LLM as a Tool to Create Reasoning Agents as MASTERMIND

Introduction: article created as first test of GPT-RESEARCHER as a research tool The integration of Retrieval-Augmented Generative Engine (RAGE) with Large Language Models (LLMs) represents a significant advancement in the field of artificial intelligence, particularly in enhancing the reasoning capabilities of these models. This report delves into the application of RAGE in transforming LLMs into sophisticated reasoning agents, akin to a “MASTERMIND,” capable of strategic reasoning and intelligent decision-making. The focus is on how RAG […]

Learn More
Close Menu