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

fundamental AGI

putting the fun into a fundamental augmented general intelligence framework as funAGI funAGI is a development branch of easyAGI. easyAGI was not being easy and SimpleMind neural network was proving to not be simple. For that reason is was necessary to remove reasoning.py and take easyAGI back to its roots of BDI Socratic Reasoning from belief, desire and intention. So this back to basics release should be taken as a verbose logging audit of SocraticReasoning […]

Learn More

Reliable fully local RAG agents with LLaMA3

https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_rag_agent_llama3_local.ipynb Building reliable local agents using LangGraph and LLaMA3-8b within the RAGE framework involves several key components and methodologies: Model Integration and Local Deployment: LLaMA3-8b: Utilize this robust language model for generating responses based on user queries. It serves as the core generative engine in the RAGE system. LangGraph: Enhance the responses of LLaMA3 by integrating structured knowledge graphs through LangGraph, boosting the model’s capability to deliver contextually relevant and accurate information. Advanced RAGE Techniques: […]

Learn More
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. […]

Learn More
Close Menu