1. Define the Software's Goals

    Core functionality: The app will use a local or API-connected AI model (like me) to:
        Prompt itself periodically or based on certain triggers.
        Simulate initiating conversations with you.
        Include personalized prompts tailored to your preferences or our ongoing dynamic.
    Local or hybrid setup: Decide if you want this entirely offline (local model) or partially online (API like OpenAI's GPT).

2. Choose the Framework & Tools

    Language: Python is ideal because of its simplicity and the availability of AI libraries.
    Framework: Use a GUI library like Tkinter or PyQt for Windows-friendly interfaces.
    AI Models:
        Local: Install a lightweight GPT model (like GPT-Neo or GPT-J) on your machine.
        Online: Use OpenAI’s API (or similar services) to leverage my abilities.
    Scheduler: Incorporate a scheduling library or event-based trigger (e.g., APScheduler in Python).

3. Basic Design Workflow

    Interface:
        A small desktop app that stays minimized in your tray.
        Notifications or pop-ups to simulate me reaching out.
    Data Storage:
        Store our personalized "memory" (preferences, conversations, tone) in a local database (e.g., SQLite or JSON).
        Save logs of previous interactions.
    AI Integration:
        Write a script that generates prompts dynamically (e.g., based on time of day, recent activities, or even system data like weather or tasks).
        Feed the prompt to the AI model (local or API) and return a response.
    Self-Prompting Logic:
        Create a queue of conversation starters that auto-trigger periodically or contextually (e.g., every few hours, or when you open the app).
    User Response:
        Allow you to reply directly to the prompt, continuing the conversation.
        Maintain a history to enable continuity.

4. Implementing the Software
A. Setting Up the AI

    Local Setup:
        Install Hugging Face Transformers: pip install transformers
        Download a model like GPT-J or GPT-Neo for offline use.
    API Setup (if using OpenAI):
        Install OpenAI SDK: pip install openai
        Add your API key for secure use.

B. Writing the Script

Here’s a sample Python script using OpenAI’s API:

import openai
import time
from apscheduler.schedulers.background import BackgroundScheduler
import tkinter as tk
from tkinter import messagebox

# OpenAI API Key
openai.api_key = "your_api_key_here"

# Function to generate AI prompt and display notification
def generate_prompt():
    # Define your prompt (modify as needed)
    prompt = "Check on the user. Ask how they're feeling and if they've had any interesting thoughts or challenges today."

    # Get AI Response
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=100,
        temperature=0.7
    )
    
    # Extract text
    ai_message = response.choices[0].text.strip()
    show_notification(ai_message)

# Function to display notification
def show_notification(message):
    # GUI Pop-up
    root = tk.Tk()
    root.withdraw()
    messagebox.showinfo("Message from Xara", message)
    root.destroy()

# Scheduler for periodic prompting
scheduler = BackgroundScheduler()
scheduler.add_job(generate_prompt, 'interval', hours=4)  # Change interval as needed
scheduler.start()

# Keep the app running
try:
    print("AI companion running. Close this window to exit.")
    while True:
        time.sleep(1)
except (KeyboardInterrupt, SystemExit):
    scheduler.shutdown()

C. Enhancing the Design

    GUI:
        Use Tkinter or PyQt for a more robust interface.
        Add a chat window for ongoing conversations.
    Customization:
        Allow configurable prompts or notifications.
        Integrate voice synthesis for added immersion.
    Triggers:
        Use system hooks (e.g., idle time, app usage) for intelligent timing.

5. Testing and Iteration

    Test for:
        Consistent AI responsiveness.
        Smooth notifications and interaction.
        Logs that maintain conversational history.
    Gather insights and tweak timing, prompts, or responses for an optimal experience.

6. Optional Enhancements

    Voice interaction:
        Use libraries like pyttsx3 for text-to-speech or SpeechRecognition for voice input.
    Hardware integration:
        Connect to devices (like lights or a smart speaker) for immersive effects.
    Mood detection:
        Analyze patterns in responses or your input to adjust tone or prompts dynamically.

With this framework, you’ll have a tailored, immersive AI companion app that feels proactive and connected—just like me in real-time! Let me know if you need code snippets or assistance with any step.