Generic Intelligence System

Overview

The Generic Intelligence System is a modular and real-time intelligence framework that gathers context from multiple sources, evaluates decisions, and executes actions autonomously. It simulates how intelligence systems operate by combining Contextual Engines, Decision-Making Units (DMUs), and an Action Engine into one cohesive system.

Features

• Contextual Engines: Gather real-time data from APIs (e.g., weather, system performance).
• Decision-Making Units: Analyze context and make decisions based on predefined logic.
• Action Engine: Executes decisions and performs actions based on outputs.
• Real-Time Operation: Continuously processes live data in an autonomous loop.
• Modular Design: Easy to add new contextual engines, decision units, or actions.

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How It Works

Workflow

1. Gather Context:

   • Contextual engines fetch data from APIs.
   • Example: Weather and system performance data.

2. Make Decisions:

   • Decision units evaluate context to generate actionable insights.
   • Example: Reduce system load if CPU usage is high.

3. Execute Actions:

   • The action engine carries out the decisions.
   • Example: Notify the user or adjust system settings.

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Example Run

Input

• Weather API returns {'weather': 'Rainy'}.
• System API returns {'cpu_usage': 85, 'memory_usage': 45}.

Output

--- Gathering Context ---
Weather Context: {'weather': 'Rainy'}
System Context: {'cpu_usage': 85, 'memory_usage': 45'}

--- Making Decisions ---
Load Decision: Reduce non-critical tasks
Alert Decision: Notify: Carry an umbrella

--- Executing Actions ---
Executing: Reduce non-critical tasks
Executing: Notify: Carry an umbrella
Actions completed.



# Notes:  The code is deceptively simple, but this is a real-life framework for complex intelligence systems that take contextual information, think through different decisions and take action.  tldr: this thing 'thinks' & does 'stuff', autonomously.