Professor Codephreak

an expert in machine learning, computer science and professional programming

Professor Codephreak Software Engineer Machine Learning Platform Architect

Related articles

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 […]

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workflow

workflow for providing solution from AGI as a response from reasoning

To provide a solution that processes user input through various reasoning methods, then integrates the decision-making with the Socratic reasoning process to provide a final AGI response, follow this workflow. This will involve updates to several modules and integrating logging and reasoning processes. Here’s the detailed workflow: Workflow Steps: Workflow Roadmap from UI to AGI Solution: By following this workflow, the system ensures that user input is processed through multiple reasoning methods, validated and refined […]

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fundamentalAGI

FundamentalAGI Blueprint

funAGI Objective: Develop a comprehensive Autonomous General Intelligence (AGI) system named FundamentalAGI (funAGI). This system integrates various advanced AI components to achieve autonomous general intelligence, leveraging multiple frameworks, real-time data processing, advanced reasoning, and a sophisticated memory system. Design will be modular for dynamic adaptation using modern object oriented programming technique primary in the Python language. Components of funAGI: the big picture Detailed Architecture and Implementation Plan 1. Cognitive Architecture 2. Multi-Modal and Multi-Model Integration […]

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