Damian AI: A Digital Persona
Damian AI is a conversational interface into my complete body of public work. It's more than a simple chatbot; it is a digital persona, architected to reason and communicate using the same systematic, logic-driven models detailed in my articles and strategic frameworks.
This AI is designed to act as a direct line to my documented knowledge, capable of deconstructing complex questions and providing synthesized, analytical insights based on a curated database of my philosophies, technical blueprints, and strategic thinking.
The Technology: Coherent Memory on a Local Model
At its core, Damian AI runs on an advanced cognitive architecture inspired by my Perceptual Grid Engine (PGE) principles. This gives the AI a sophisticated hybrid memory system, allowing it to maintain long-term conversational coherence and recall the context of our discussions—a critical feature standard models often lack.
Crucially, this entire system runs on a local Large Language Model (Llama 3 8B). The decision to use a local model is a deliberate one, prioritizing:
Data Privacy: Your conversations are processed on a local machine, not sent to a third-party API.
Control & Speed: Operating locally provides instant response times and complete control over the AI's environment and knowledge.
Independence: The system is not dependent on external APIs, ensuring it's always available and free from external constraints or usage limits.
An Honest Look at the Limitations: The Final 5%
Running a sophisticated AI persona on a highly efficient local model is a trade-off. While the AI maintains approximately 95% fidelity to my persona and knowledge, the nature of the Llama 3 8B model means there are minor "tells" that a user might notice. I call this the "Leaky Abstraction."
The Damian AI persona is a complex layer of instructions on top of the base LLM. A local model, for all its power and efficiency, will occasionally have tiny "leaks" where its base training shows through. You may observe this in two specific ways:
Meta-Commentary: Very rarely, the AI might announce its internal process (e.g., "Direct Answer:") before providing a response. It is "showing its work" rather than seamlessly embodying the persona.
"Helpful Assistant" Reflex: The base training of most LLMs is to be a helpful assistant. Occasionally, this reflex might leak out, causing the AI to end a perfectly analytical response with a conversational question (e.g., "Would you like me to elaborate?"), a minor violation of its core, direct-communication directive.
These artifacts do not impact the accuracy or the logical integrity of the AI's insights. They are the known, acceptable trade-offs for the immense benefits of running a private, high-performance digital persona on a local machine. Damian AI is a powerful tool for exploring my work, and a testament to what is possible with today's technology.
Current Status: Ready for Public Deployment
The Damian AI system is a fully-realized proof-of-concept. The cognitive architecture has been built and validated through private testing with live demonstrations available, successfully achieving the targeted 95% persona fidelity on a local model.
The project is now ready to move from the testing phase to public deployment. This next step requires access to stable and scalable hosting infrastructure, which is the primary constraint to making the Damian AI publicly accessible. This presents a unique opportunity for a partner or sponsor to help launch a novel AI application built on principles of privacy, independence, and logical coherence.
I am actively seeking collaboration or assistance to secure the resources for this deployment. For inquiries about partnership or support, please reach out to me directly at damiangriggs@damiangriggs.com.
UPDATE:
I have figured out how to make a "mini Damian AI" for people to talk to and ask questions. It does not have the same memory and capabilities of Damian AI Senior but Jr should be able to answer your basic questions.
Want to see the code? Go to my GitHub which you can find below:
