class EigenAI:  # ❌ Container
    def __init__(self):
        self.language_layer = {}  # ❌ Layer container
        self.universal_layer = {}  # ❌ Layer container
        self.graph_layer = {}     # ❌ Layer container
        self.query_layer = {}     # ❌ Layer container

# "proof" concept = node
proof_concept = {
    'type': 'concept',
    'term': 'proof',
    'domains': [],  # Which domains use this concept
    'series': [],   # Evolution history
    'links': []     # Related concepts
}

# Track usage across domains
proof_concept['series'].append({
    't': 0,
    'domain_count': 0,
    'total_occurrences': 0,
    'confidence': 0.0
})

# After learning from crypto domain
proof_concept['domains'].append('cryptography')
proof_concept['series'].append({
    't': 100,
    'domain_count': 1,
    'total_occurrences': 47,
    'confidence': 0.78
})

# After learning from math domain
proof_concept['domains'].append('mathematics')
proof_concept['series'].append({
    't': 200,
    'domain_count': 2,
    'total_occurrences': 156,
    'confidence': 0.92,
    'universal': True  # Appears in multiple domains!
})

# "cryptography" domain = node
crypto_domain = {
    'type': 'domain',
    'name': 'cryptography',
    'series': [],
    'links': []  # Concepts in this domain
}

# Domain evolution
crypto_domain['series'].append({
    't': 0,
    'concepts': 0,
    'corpus_size': 0,
    'confidence': 0.0
})

crypto_domain['series'].append({
    't': 500,
    'concepts': 1234,
    'corpus_size': 50_000_000,  # 50MB learned
    'confidence': 0.89,
    'queries_served': 456
})

# Relationship between concepts = node
proof_of_stake = {
    'type': 'relationship',
    'from_concept': 'proof',
    'to_concept': 'stake',
    'series': [],
    'links': []
}

# Track co-occurrence strength
proof_of_stake['series'].append({
    't': 0,
    'co_occurrences': 0,
    'strength': 0.0
})

proof_of_stake['series'].append({
    't': 300,
    'co_occurrences': 89,
    'strength': 0.87,
    'contexts': ['blockchain', 'consensus']
})

# Query = node created when user asks
query_node = {
    'type': 'query',
    'text': 'Explain Proof of Stake',
    'keywords': ['proof', 'stake'],
    'series': [],
    'links': []  # Links to concept nodes used
}

# Query processing creates series
query_node['series'].append({
    't': 1000,
    'concepts_found': ['proof', 'stake', 'consensus'],
    'domains_involved': ['cryptography', 'blockchain'],
    'confidence': 0.94,
    'response_generated': True
})

# t=0: Initialize empty graph
intelligence_graph = []

# Learn from corpus (e.g., crypto textbooks)
corpus = load_corpus('bitcoin-whitepaper.txt')

# Extract concepts = create concept nodes
def learn_from_corpus(graph, corpus, domain):
    """
    Learn = create concept nodes from corpus
    
    NOT: Store in layer containers
    BUT: Create nodes for each discovered concept
    """
    # Extract concepts from text
    concepts = extract_concepts(corpus)
    
    for concept_text in concepts:
        # Create or find concept node
        concept_node = find_or_create_concept(graph, concept_text)
        
        # Add domain if new
        if domain not in concept_node['domains']:
            concept_node['domains'].append(domain)
        
        # Update series
        concept_node['series'].append({
            't': len(graph),
            'domain': domain,
            'occurrences': count_occurrences(concept_text, corpus),
            'context': extract_context(concept_text, corpus)
        })
        
        # Link to domain node
        domain_node = find_or_create_domain(graph, domain)
        create_link(concept_node, domain_node, 'appears_in')
    
    # Discover relationships
    for concept1, concept2 in find_co_occurrences(concepts, corpus):
        relationship_node = create_relationship(graph, concept1, concept2)
        relationship_node['series'].append({
            't': len(graph),
            'domain': domain,
            'strength': calculate_strength(concept1, concept2, corpus)
        })
    
    return graph

# Learn!
intelligence_graph = learn_from_corpus(intelligence_graph, corpus, 'cryptography')

# Graph grows additively

def find_universal_concepts(intelligence_graph, threshold=0.6):
    """
    Find concepts appearing in multiple domains
    
    Like pidgin emergence from language intersection (Post 830)
    """
    # Get all concept nodes
    concept_nodes = [n for n in intelligence_graph if n['type'] == 'concept']
    
    # Get all domains
    all_domains = set()
    for node in intelligence_graph:
        if node['type'] == 'domain':
            all_domains.add(node['name'])
    
    total_domains = len(all_domains)
    
    universal_concepts = []
    
    for concept in concept_nodes:
        # How many domains use this concept?
        domain_count = len(concept['domains'])
        coverage = domain_count / total_domains
        
        if coverage >= threshold:
            # Mark as universal
            concept['universal'] = True
            concept['coverage'] = coverage
            
            # Update series
            concept['series'].append({
                't': current_time(),
                'status': 'universal',
                'domain_coverage': coverage,
                'total_domains': domain_count
            })
            
            universal_concepts.append(concept)
    
    return universal_concepts

# After learning from multiple domains
crypto_graph = learn_from_corpus(graph, crypto_corpus, 'cryptography')
math_graph = learn_from_corpus(graph, math_corpus, 'mathematics')
physics_graph = learn_from_corpus(graph, physics_corpus, 'physics')

# Find universals
universal = find_universal_concepts(graph, threshold=0.6)
# → ['proof', 'system', 'function', 'structure', 'process', ...]

# These nodes are gold (universal across domains)

def query_intelligence(graph, query_text):
    """
    Query = traverse graph from keyword nodes
    
    NOT: Search in layer containers
    BUT: Start at concept nodes, follow links
    """
    # Create query node
    query_node = {
        'type': 'query',
        'text': query_text,
        'keywords': extract_keywords(query_text),
        'series': [],
        'links': []
    }
    
    graph.append(query_node)
    
    # Find concept nodes for keywords
    concept_nodes = []
    for keyword in query_node['keywords']:
        node = find_concept_node(graph, keyword)
        if node:
            concept_nodes.append(node)
            # Link query to concept
            create_link(query_node, node, 'uses_concept')
    
    # Traverse from concept nodes
    context = {
        'concepts': [],
        'domains': set(),
        'relationships': [],
        'universal_concepts': [],
        'confidence': 0.0
    }
    
    for concept_node in concept_nodes:
        # Add concept
        context['concepts'].append(concept_node['term'])
        
        # Add domains this concept appears in
        context['domains'].update(concept_node['domains'])
        
        # Check if universal
        if concept_node.get('universal', False):
            context['universal_concepts'].append(concept_node['term'])
        
        # Follow links to related concepts (depth 2)
        for link in concept_node['links']:
            if link['type'] == 'relates_to':
                related_node = find_node_by_id(graph, link['to'])
                context['concepts'].append(related_node['term'])
                
                # Add relationship
                context['relationships'].append({
                    'from': concept_node['term'],
                    'to': related_node['term'],
                    'strength': link['strength']
                })
    
    # Calculate confidence from graph structure
    context['confidence'] = calculate_confidence(context)
    
    # Update query node series
    query_node['series'].append({
        't': len(graph),
        'concepts_found': len(context['concepts']),
        'domains': list(context['domains']),
        'confidence': context['confidence']
    })
    
    return context

# Example
context = query_intelligence(graph, "Explain Proof of Stake")
# → {
#     'concepts': ['proof', 'stake', 'consensus', 'validator', ...],
#     'domains': {'cryptography', 'blockchain'},
#     'universal_concepts': ['proof', 'system'],
#     'confidence': 0.94
#   }

# Someone wants to add biology intelligence
# They just create new nodes!

def add_biology_intelligence(shared_graph, bio_corpus):
    """
    Add biology intelligence = add biology nodes to graph
    
    NOT: Create new EigenAI subclass
    BUT: Add nodes with biology domain
    """
    # Learn biology concepts (creates nodes)
    bio_graph = learn_from_corpus(shared_graph, bio_corpus, 'biology')
    
    # Biology concepts now in shared graph
    # Universal concepts automatically discovered
    # Cross-domain queries now work!
    
    return bio_graph

# Add biology
bio_nodes = add_biology_intelligence(intelligence_graph, bio_corpus)

# Add code
code_nodes = add_biology_intelligence(intelligence_graph, code_corpus)

# Add philosophy  
phil_nodes = add_biology_intelligence(intelligence_graph, phil_corpus)

# Graph naturally grows
# Universal concepts emerge at intersections
# No modification of existing nodes needed

# Each specialization = filtered view of shared graph

class IntelligenceSpecialization:
    """
    Specialization = subset of nodes focused on domain
    
    NOT a separate system
    BUT a view into shared graph
    """
    
    def __init__(self, shared_graph, domain):
        self.shared_graph = shared_graph
        self.domain = domain
    
    def get_domain_nodes(self):
        """Get nodes relevant to this domain"""
        return [
            n for n in self.shared_graph
            if self.domain in n.get('domains', [])
        ]
    
    def query(self, query_text):
        """Query focused on this domain"""
        # Get domain-specific nodes
        domain_nodes = self.get_domain_nodes()
        
        # Query using domain subgraph
        context = query_intelligence(domain_nodes, query_text)
        
        # But can still access universal concepts from shared graph
        universal = [
            n for n in self.shared_graph
            if n.get('universal', False)
        ]
        
        # Combine domain + universal
        return {
            'domain_context': context,
            'universal_available': universal,
            'confidence': context['confidence']
        }

# Create specializations (views, not copies)
crypto_ai = IntelligenceSpecialization(intelligence_graph, 'cryptography')
bio_ai = IntelligenceSpecialization(intelligence_graph, 'biology')
code_ai = IntelligenceSpecialization(intelligence_graph, 'code')

# Each queries its subdomain
crypto_answer = crypto_ai.query("What is Proof of Stake?")
bio_answer = bio_ai.query("How do cells divide?")

# But they share universal concepts

def calculate_confidence(context):
    """
    Confidence emerges from graph structure
    
    High confidence when:
    - Many concept nodes found
    - Strong relationships exist
    - Multiple domains involved
    - Universal concepts present
    """
    # Concept coverage
    concept_score = min(len(context['concepts']) / 10.0, 1.0)
    
    # Domain coverage
    domain_score = min(len(context['domains']) / 3.0, 1.0)
    
    # Relationship strength
    if context['relationships']:
        avg_strength = sum(r['strength'] for r in context['relationships']) / len(context['relationships'])
        relationship_score = avg_strength
    else:
        relationship_score = 0.0
    
    # Universal concept bonus
    universal_score = min(len(context['universal_concepts']) / 2.0, 1.0)
    
    # Weighted combination
    confidence = (
        concept_score * 0.3 +
        domain_score * 0.25 +
        relationship_score * 0.25 +
        universal_score * 0.2
    )
    
    return confidence

# Example: High confidence query
context = query_intelligence(graph, "Compare evolution to optimization")
# Concepts: evolution, optimization, selection, adaptation, fitness
# Domains: biology, computer-science, mathematics
# Universal: system, function, process
# Confidence: 0.95 ✅

# Example: Low confidence query
context = query_intelligence(graph, "Explain quantum knitting")
# Concepts: quantum (found), knitting (not found)
# Domains: physics (partial)
# Universal: none relevant
# Confidence: 0.23 ❌

# Each node announces its capabilities
def announce_to_dht(intelligence_graph, eigendht):
    """
    Announce which domains this intelligence graph covers
    """
    # Calculate statistics
    domains = set()
    concept_count = 0
    universal_count = 0
    
    for node in intelligence_graph:
        if node['type'] == 'domain':
            domains.add(node['name'])
        elif node['type'] == 'concept':
            concept_count += 1
            if node.get('universal', False):
                universal_count += 1
    
    # Announce to DHT
    announcement = {
        'node_type': 'eigenai',
        'domains': list(domains),
        'concept_count': concept_count,
        'universal_count': universal_count,
        'confidence_avg': calculate_average_confidence(intelligence_graph),
        'queries_served': get_query_count(intelligence_graph)
    }
    
    eigendht.announce(announcement)
    
    return announcement

# User queries DHT
query = "Explain photosynthesis"
eigendht_query = {
    'keywords': ['photosynthesis'],
    'required_domains': ['biology']
}

# DHT finds nodes with biology domain
matching_nodes = eigendht.find_nodes(eigendht_query)
# → [node_A (biology), node_B (biology+chemistry), ...]

# Route to highest confidence node
best_node = max(matching_nodes, key=lambda n: n['confidence_avg'])

def store_intelligence_graph(graph):
    """
    Store intelligence graph in R³
    
    Each node = series stored separately
    Links = references
    """
    for node in graph:
        if node['type'] == 'concept':
            r3_store(
                key=f"concept:{node['term']}",
                series=node['series'],
                links=node['links'],
                metadata={'domains': node['domains'], 'universal': node.get('universal', False)}
            )
        
        elif node['type'] == 'domain':
            r3_store(
                key=f"domain:{node['name']}",
                series=node['series'],
                links=node['links']
            )
        
        elif node['type'] == 'relationship':
            r3_store(
                key=f"rel:{node['from_concept']}:{node['to_concept']}",
                series=node['series'],
                links=node['links']
            )
        
        elif node['type'] == 'query':
            r3_store(
                key=f"query:{hash(node['text'])}",
                series=node['series'],
                links=node['links']
            )

def load_intelligence_graph(domain=None):
    """
    Load intelligence graph from R³
    
    Can load specific domain or entire graph
    """
    if domain:
        # Load domain subgraph
        domain_node = r3_load(f"domain:{domain}")
        
        # Load concepts in this domain
        concepts = []
        for link in domain_node['links']:
            if link['type'] == 'contains':
                concept = r3_load(f"concept:{link['to']}")
                concepts.append(concept)
        
        return {'domain': domain_node, 'concepts': concepts}
    else:
        # Load entire graph (or use DHT to discover nodes)
        all_nodes = r3_query("type:*")
        return all_nodes

# Expansion 1: Add new domain
def add_medical_intelligence(graph, medical_corpus):
    # Just learn from corpus
    # Creates medical concept nodes
    # Links to existing universal concepts
    # No modification of existing nodes
    return learn_from_corpus(graph, medical_corpus, 'medicine')

# Expansion 2: Add new concept type
def add_temporal_concepts(graph):
    """
    Add time-aware concept nodes
    """
    for concept_node in graph:
        if concept_node['type'] == 'concept':
            # Add temporal tracking
            temporal_node = {
                'type': 'temporal_concept',
                'base_concept': concept_node['term'],
                'series': [],  # Track how concept evolved over time
                'links': []
            }
            
            # Link to base concept
            create_link(temporal_node, concept_node, 'temporal_view')
            
            graph.append(temporal_node)
    
    return graph

# Expansion 3: Add reasoning nodes
def add_reasoning_nodes(graph):
    """
    Add nodes that represent logical inferences
    """
    for concept1 in get_concepts(graph):
        for concept2 in get_related_concepts(concept1):
            # Can we infer concept2 from concept1?
            if can_infer(concept1, concept2):
                inference_node = {
                    'type': 'inference',
                    'premise': concept1['term'],
                    'conclusion': concept2['term'],
                    'series': [],
                    'links': []
                }
                
                graph.append(inference_node)
    
    return graph

# All additive!
# No existing nodes modified
# New capabilities just add new node types

# Query spanning multiple domains
query = "Compare biological evolution to code refactoring"

# Traditional AI: Doesn't understand connection
# EigenAI graph: Finds universal concepts

# Query processing:
keywords = ['biological', 'evolution', 'code', 'refactoring']

# Find concept nodes
bio_concepts = find_concepts(graph, 'biology')
code_concepts = find_concepts(graph, 'programming')

# Find intersection (universal concepts)
shared = find_universal_in_both(bio_concepts, code_concepts)
# → ['adaptation', 'optimization', 'selection', 'improvement', 'iteration']

# Generate response using:
# - Biology concepts (evolution, species, fitness)
# - Code concepts (refactoring, patterns, optimization)
# - Universal concepts (adaptation, selection, improvement)
# - Relationships between them

response = {
    'comparison': {
        'biological_evolution': {
            'process': 'Natural selection improves species fitness',
            'concepts': ['mutation', 'selection', 'adaptation'],
            'domain': 'biology'
        },
        'code_refactoring': {
            'process': 'Developers improve code quality',
            'concepts': ['patterns', 'optimization', 'simplification'],
            'domain': 'programming'
        },
        'universal_patterns': [
            'Both iteratively improve through selection',
            'Both adapt to environment/requirements',
            'Both optimize for fitness/quality',
            'Both accumulate improvements over time'
        ]
    },
    'confidence': 0.91,
    'domains_used': ['biology', 'programming'],
    'universal_concepts_used': ['adaptation', 'optimization', 'selection']
}

# Only possible because graph structure reveals universal patterns!

# Deploy intelligence node
def deploy_intelligence_node(domain, corpus, stake):
    """
    Deploy = create specialized intelligence subgraph
    """
    # Create graph
    graph = []
    
    # Learn from corpus (creates nodes)
    graph = learn_from_corpus(graph, corpus, domain)
    
    # Find universal concepts
    universal = find_universal_concepts(graph)
    
    # Calculate quality metrics
    metrics = {
        'concepts': len([n for n in graph if n['type'] == 'concept']),
        'domains': len(set(n.get('domain', '') for n in graph)),
        'universal': len(universal),
        'confidence_avg': calculate_average_confidence(graph)
    }
    
    # Stake to serve
    staking_record = {
        'graph': graph,
        'domain': domain,
        'stake': stake,
        'metrics': metrics,
        'queries_served': 0,
        'revenue': 0
    }
    
    # Announce to DHT
    eigendht.announce(staking_record)
    
    return staking_record

# Example
crypto_node = deploy_intelligence_node(
    domain='cryptography',
    corpus=load_corpus('crypto-textbooks'),
    stake=150  # EIGEN tokens
)

# Serve queries
query_result = query_intelligence(crypto_node['graph'], "Explain PoS")

# Earn revenue
crypto_node['queries_served'] += 1
crypto_node['revenue'] += 0.1  # EIGEN per query

# Higher confidence → More queries routed → Higher revenue

# Initialize shared graph
shared_intelligence_graph = []

# Node 1: Crypto specialist
crypto_graph = learn_from_corpus(
    shared_intelligence_graph,
    load_corpus('bitcoin-whitepaper.txt'),
    'cryptography'
)

# Node 2: Biology specialist
bio_graph = learn_from_corpus(
    shared_intelligence_graph,
    load_corpus('biology-textbook.txt'),
    'biology'
)

# Node 3: Code specialist
code_graph = learn_from_corpus(
    shared_intelligence_graph,
    load_corpus('github-repos.txt'),
    'programming'
)

# Universal concepts emerge automatically
universal = find_universal_concepts(shared_intelligence_graph, threshold=0.6)
# → ['system', 'function', 'process', 'structure', 'optimization', ...]

# Query 1: Domain-specific
result = query_intelligence(shared_intelligence_graph, "What is SHA-256?")
# Domains: cryptography
# Confidence: 0.95
# Uses: crypto-specific concepts

# Query 2: Cross-domain
result = query_intelligence(
    shared_intelligence_graph,
    "How is blockchain like DNA?"
)
# Domains: cryptography, biology
# Confidence: 0.87
# Universal concepts: system, replication, information, verification
# Shows: Both store information, both replicate, both have verification

# Query 3: Novel connection
result = query_intelligence(
    shared_intelligence_graph,
    "Apply genetic algorithms to optimize smart contracts"
)
# Domains: biology, programming, cryptography
# Confidence: 0.79
# Universal concepts: optimization, selection, adaptation, evolution
# Combines all three domains using universal patterns!