Discover how the CAM Objective Function redefines AI optimization by aligning key elements like loss functions, world models, and ethical oversight. This structured, multi-layered approach improves LLM performance through...
Author - John Deacon
John is a researcher and digitally independent practitioner focused on developing aligned cognitive extension technologies. His creative and technical work draws from industry experience across instrumentation, automation and workflow engineering, systems dynamics, and strategic communications design.
Rooted in the philosophy of Strategic Thought Leadership, John's work bridges technical systems, human cognition, and organizational design, helping individuals and enterprises structure clarity, alignment, and sustainable growth into every layer of their operations.
Discover how the Core Alignment Model (CAM) uses a Dynamic Attractor and Semantic Distillation to transform noisy LLM outputs into purpose-driven, ethically sound responses. By filtering through layered adaptive processes, CAM...
As the Semantic Distillation process moves through each layer, CAM progressively channels raw LLM output from a broad, often noisy initial state toward a refined, high-integrity response. Each layer’s unique role allows it to act as a...