📱 Zero-Prompt Social Media

The Future of Social Media

Current AI: "Write me a story about..." → Generated content
PLGL Social: Just swipe! → Infinite personalized AI content

PLGL removes the biggest friction in generative AI - the need to describe what you want. Just like TikTok learns from your swipes, PLGL learns your content preferences and generates exactly what you'll love.

How Zero-Prompt Social Works

1. No Sign-Up Questionnaires

Start with diverse AI-generated content immediately:

class ZeroPromptFeed:
    """Infinite personalized content without prompting"""
    
    def start_new_session(self, user_id: str):
        """Just start swiping - no setup needed!"""
        
        # Generate diverse initial content
        initial_content = []
        
        for i in range(10):
            # Diverse sampling strategy
            if i < 3:
                z = torch.randn(1, latent_dim)  # Random
            elif i < 6:
                z = torch.randn(1, latent_dim) * 0.7  # Semi-random
            else:
                z = torch.randn(1, latent_dim) * 0.3  # Near center
            
            # Generate content (video, music, story, etc.)
            content = self.generator(z)
            initial_content.append(content)
        
        return initial_content

2. Learn From Natural Interactions

Every swipe, like, and view duration trains your preference model:

def swipe(self, user_id: str, content_item: ContentItem, 
          action: str, view_duration: float):
    """
    Learn from user interactions:
    - 'like': Strong positive signal
    - 'skip': Negative signal  
    - 'share': Very strong positive
    - View duration: Engagement metric
    """
    
    # Convert interaction to preference score
    if action == 'like':
        preference = 0.8
    elif action == 'share' or action == 'save':
        preference = 1.0
    elif action == 'skip' and view_duration < 3.0:
        preference = 0.2
    else:
        # Watched but didn't engage
        preference = 0.5 + (view_duration / 30.0) * 0.3
    
    # Update preference model
    self.update_preferences(user_id, content_item.latent, preference)
    
    # Generate next content based on updated preferences
    return self.generate_next_content(user_id)

3. Real-Time Adaptation

The feed adapts immediately to your preferences:

def generate_next_content(self, user_id: str) -> ContentItem:
    """Generate personalized content in real-time"""
    
    preference_model = self.preference_models[user_id]
    
    # 70% exploitation (content you'll love)
    if random.random() < 0.7:
        # Optimize for high preference score
        z = self.find_optimal_latent(preference_model)
    else:
        # 30% exploration (discover new interests)
        z = self.smart_exploration(preference_model)
    
    # Generate content
    content = self.generator(z)
    
    # Predict engagement
    predicted_score = preference_model(z)
    
    return ContentItem(
        latent=z,
        content=content,
        predicted_engagement=predicted_score
    )

4. Multi-Modal Content Generation

Support different types of AI-generated content:

class MultiModalFeed:
    def __init__(self):
        self.generators = {
            'video': VideoVAE(),      # Short-form videos
            'music': MusicVAE(),      # Background tracks
            'story': StoryGPT(),      # Text narratives
            'image': ImageGAN(),      # Visual content
            'meme': MemeGenerator()   # Humor content
        }
        
        # Separate preference model for each content type
        self.preference_models = {
            content_type: PreferenceModel()
            for content_type in self.generators
        }
    
    def generate_mixed_feed(self, user_id: str):
        """Mix different content types based on preferences"""
        
        # Learn which content types user prefers
        type_preferences = self.get_content_type_preferences(user_id)
        
        # Generate feed with preferred mix
        feed = []
        for _ in range(10):
            # Sample content type based on preferences
            content_type = self.sample_content_type(type_preferences)
            
            # Generate content of that type
            content = self.generate_content(user_id, content_type)
            feed.append(content)
        
        return feed

5. Viral Mechanics Without Virality

Every piece of content is unique to you, yet feels viral:

def add_social_signals(self, content: ContentItem, user_id: str):
    """Add personalized 'social' elements"""
    
    # Generate AI comments that match user's humor style
    comments = self.generate_comments(content, user_id)
    
    # Create synthetic engagement metrics
    # (makes content feel popular without being viral)
    content.likes = self.generate_engagement_count(content)
    content.views = content.likes * random.randint(10, 100)
    
    # Add personalized hashtags
    content.hashtags = self.generate_hashtags(content, user_id)
    
    return content

Real-World Applications

Privacy-First Design

class PrivatePreferenceLearning:
    """All preference learning happens on-device"""
    
    def __init__(self):
        # Lightweight models run locally
        self.local_preference_model = CompactNN(params=50000)
        
        # Only latent codes are sent to server
        # Never raw preferences or personal data
        self.sync_only_latents = True
        
    def federated_learning_update(self):
        """Share learning without sharing data"""
        
        # Compute gradient updates locally
        local_gradients = self.compute_gradients()
        
        # Add noise for differential privacy
        private_gradients = self.add_privacy_noise(local_gradients)
        
        # Only send anonymous updates
        return private_gradients

Why This Changes Everything

• Zero Friction: No prompts, descriptions, or searches needed
• Infinite Content: Every piece is unique and personalized
• Perfect Discovery: Find content you didn't know you wanted
• No Filter Bubbles: Controlled exploration prevents echo chambers
• Privacy Preserved: Learn preferences without tracking behavior
• No Creator Burnout: Infinite AI creators that never get tired