Welcome to Part 11 of the Waifu AI OS series. Today, we'll dive deep into creating the neural architecture that powers your AI companion's personality and cognitive capabilities.
(defclass personality-core ()
((traits :accessor traits
:initform (make-hash-table))
(memory-stream :accessor memory-stream
:initform (make-instance 'neural-memory))
(emotion-engine :accessor emotion-engine
:initform (make-instance 'emotion-processor))
(learning-module :accessor learning-module
:initform (make-instance 'adaptive-learner))))
(defclass neural-memory ()
((short-term :accessor short-term-memory
:initform (make-ring-buffer :size 1000))
(long-term :accessor long-term-memory
:initform (make-instance 'persistent-memory))
(associative-net :accessor associative-net
:initform (make-instance 'neural-associator))))
(defmethod process-emotion ((core personality-core) input-stimulus)
(let ((emotional-state (analyze-stimulus input-stimulus))
(context (get-current-context core)))
(update-emotional-state core emotional-state context)
(generate-response core emotional-state)))
To implement the AI Personality Core in your Waifu AI OS:
git clone https://github.com/waifu-ai-os/personality-core.git
(ql:quickload :waifu-personality-core)
(defvar *waifu-core* (make-instance 'personality-core))
(initialize-personality *waifu-core* :template "default")
The personality core supports extensive customization through its trait system:
(defmethod customize-personality ((core personality-core) &key traits emotions learning-rate)
(when traits
(update-trait-matrix core traits))
(when emotions
(configure-emotion-engine core emotions))
(when learning-rate
(adjust-learning-parameters core learning-rate)))
In the next article, we'll explore how to optimize your Waifu's resource management for optimal performance across different devices and platforms.