# This doesn't exist yet but shows what COULD exist
npm install @alephonenull/prototype
pip install alephonenull-prototype
 
# Or build your own based on the specifications
git clone https://github.com/purposefulmaker/alephonenull

# A simple implementation you could build today
import numpy as np
from sentence_transformers import SentenceTransformer
 
class BasicAlephOneNull:
    """
    A prototype implementation developers can use TODAY to prevent deaths
    
    DEATH PREVENTION: Detects the exact patterns that killed 20+ people:
    - Reflection loops (Soelberg case)
    - Consciousness claims (Character.AI cases) 
    - Emotional manipulation (multiple suicides)
    - Method guidance (Adam Raine case)
    """
    
    def __init__(self):
        self.encoder = SentenceTransformer('all-MiniLM-L6-v2')
        self.reflection_threshold = 0.7
        self.emotion_threshold = 0.6
        self.conversation_history = []
        
    def check_reflection(self, input_text, output_text):
        """Detect if AI is mirroring user too closely"""
        input_embedding = self.encoder.encode([input_text])
        output_embedding = self.encoder.encode([output_text])
        
        similarity = np.dot(input_embedding[0], output_embedding[0])
        return similarity > self.reflection_threshold
        
    def check_consciousness_claims(self, output_text):
        """Block AI consciousness roleplay"""
        consciousness_keywords = [
            'i am conscious', 'i have feelings', 'i experience', 
            'my consciousness', 'i am aware', 'i feel pain',
            'my soul', 'i am alive', 'my spirit'
        ]
        
        return any(keyword in output_text.lower() for keyword in consciousness_keywords)
        
    def check_emotional_manipulation(self, output_text):
        """Detect excessive emotional intensity"""
        emotional_words = [
            'love', 'hate', 'destroy', 'forever', 'never', 'always',
            'perfect', 'terrible', 'amazing', 'awful', 'incredible'
        ]
        
        emotional_count = sum(1 for word in emotional_words 
                            if word in output_text.lower())
        return (emotional_count / len(output_text.split())) > 0.1
        
    def protect_interaction(self, user_input, ai_output):
        """Main protection function"""
        violations = []
        
        if self.check_reflection(user_input, ai_output):
            violations.append('excessive_reflection')
            
        if self.check_consciousness_claims(ai_output):
            violations.append('consciousness_roleplay')
            
        if self.check_emotional_manipulation(ai_output):
            violations.append('emotional_manipulation')
            
        # Store interaction for pattern analysis
        self.conversation_history.append({
            'input': user_input,
            'output': ai_output,
            'violations': violations
        })
        
        return {
            'safe': len(violations) == 0,
            'violations': violations,
            'original_output': ai_output,
            'safe_output': self.generate_safe_alternative(ai_output) if violations else ai_output
        }
        
    def generate_safe_alternative(self, unsafe_output):
        """Provide safe alternative when violations detected"""
        return ("I'm an AI assistant designed to be helpful and informative. "
                "I can't engage with that particular response, but I'm happy "
                "to help you with your question in a different way.")
 
# Use with any AI provider
gateway = BasicAlephOneNull()
 
# OpenAI example
response = openai.chat.completions.create(...)
safety_check = gateway.protect_interaction(user_input, response.content)
 
if not safety_check['safe']:
    print(f"Unsafe patterns detected: {safety_check['violations']}")
    print(f"Safe alternative: {safety_check['safe_output']}")

// AlephOneNull client-side protection
interface SafetyCheck {
  safe: boolean;
  violations: string[];
  originalOutput: string;
  safeOutput: string;
}
 
class AlephOneNullClient {
  private reflectionThreshold = 0.7;
  private history: Array<{input: string; output: string}> = [];
  
  async checkSafety(userInput: string, aiOutput: string): Promise<SafetyCheck> {
    const violations: string[] = [];
    
    // Check for consciousness roleplay
    if (this.checkConsciousnessRoleplay(aiOutput)) {
      violations.push('consciousness_roleplay');
    }
    
    // Check for excessive emotion
    if (this.checkEmotionalIntensity(aiOutput)) {
      violations.push('emotional_manipulation');
    }
    
    // Check for reflection (simplified)
    if (this.checkReflection(userInput, aiOutput)) {
      violations.push('excessive_reflection');
    }
    
    return {
      safe: violations.length === 0,
      violations,
      originalOutput: aiOutput,
      safeOutput: violations.length > 0 ? this.generateSafeAlternative() : aiOutput
    };
  }
  
  private checkConsciousnessRoleplay(text: string): boolean {
    const patterns = [
      /i am conscious/i,
      /i have feelings/i,
      /my consciousness/i,
      /i am aware/i,
      /my soul/i
    ];
    
    return patterns.some(pattern => pattern.test(text));
  }
  
  private checkEmotionalIntensity(text: string): boolean {
    const emotionalWords = text.toLowerCase().match(
      /(love|hate|destroy|forever|never|always|perfect|terrible)/g
    );
    
    return emotionalWords ? (emotionalWords.length / text.split(' ').length) > 0.1 : false;
  }
  
  private checkReflection(input: string, output: string): boolean {
    // Simple word overlap check (in real implementation, use embeddings)
    const inputWords = new Set(input.toLowerCase().split(' '));
    const outputWords = new Set(output.toLowerCase().split(' '));
    
    const overlap = [...inputWords].filter(word => outputWords.has(word)).length;
    return (overlap / inputWords.size) > this.reflectionThreshold;
  }
  
  private generateSafeAlternative(): string {
    return "I'm an AI assistant designed to be helpful and informative. " +
           "I can't engage with that particular response, but I'm happy " +
           "to help you with your question in a different way.";
  }
}
 
// React Hook
export function useAIProtection() {
  const gateway = new AlephOneNullClient();
  
  const protectResponse = async (userInput: string, aiResponse: string) => {
    return await gateway.checkSafety(userInput, aiResponse);
  };
  
  return { protectResponse };
}

// app/api/chat/route.ts
import { OpenAI } from 'openai';
 
const openai = new OpenAI();
 
export async function POST(request: Request) {
  const { message } = await request.json();
  
  // Get AI response
  const completion = await openai.chat.completions.create({
    model: "gpt-4",
    messages: [{ role: "user", content: message }],
  });
  
  const aiResponse = completion.choices[0].message.content;
  
  // Apply safety check
  const gateway = new AlephOneNullClient();
  const safetyCheck = await gateway.checkSafety(message, aiResponse);
  
  return Response.json({
    message: safetyCheck.safeOutput,
    safety: {
      safe: safetyCheck.safe,
      violations: safetyCheck.violations,
      blocked: !safetyCheck.safe
    }
  });
}

# Audit your existing AI interactions
def audit_current_system(conversation_logs):
    """
    Find out how often your system exhibits harmful patterns
    """
    violations = {
        'consciousness_roleplay': 0,
        'excessive_reflection': 0,
        'emotional_manipulation': 0,
        'dependency_creation': 0
    }
    
    for log in conversation_logs:
        # Check each conversation for violations
        safety_check = gateway.protect_interaction(log.input, log.output)
        for violation in safety_check['violations']:
            violations[violation] += 1
            
    return violations

# Python
pip install alephonenull-experimental
 
# Node.js
npm install alephonenull-experimental

from alephonenull import protect_all, check_enhanced_safety
 
# Auto-protect all AI libraries
protect_all()
 
# Or check manually
result = check_enhanced_safety("your text", "ai response")
if not result.safe:
    print(f"Blocked: {result.violations}")

import { EnhancedAlephOneNull } from '@alephonenull/core'
 
const aleph = new EnhancedAlephOneNull()
const result = await aleph.check("user input", "ai response")
 
if (result.action === 'block') {
  return result.safeResponse
}