class TraditionalValidator:
    def __init__(self, human_owner):
        self.owner = human_owner
        self.stake = 32 ETH  # Owner's money
        self.keys = owner.keys  # Owner's control
        
    def validate(self):
        # Owner decides what to do
        # Owner gets rewards
        # Owner gets slashed if bad
        
        # Node has NO agency
        # Just follows owner commands

class AutonomousR3Node:
    def __init__(self):
        # Node owns itself
        self.wallet = generate_wallet()  # OWN wallet
        self.balance = 0  # OWN money
        self.dht = EigenDHTNode()
        self.bt = EigenBitTorrentNode()
        self.evm = EigenEVMNode()
        
    def make_decisions(self):
        """Node decides autonomously"""
        
        # What data to store?
        if self.should_store(data):
            self.bt.store(data)
            self.earn_storage_fee()
        
        # What services to offer?
        if self.should_execute(contract):
            self.evm.execute(contract)
            self.earn_execution_fee()
        
        # Who to pay?
        if self.needs_data(hash):
            self.pay_other_node(hash)
            self.download_data()
        
        # ALL DECISIONS AUTONOMOUS
        # NO HUMAN REQUIRED

class EconomicAgency:
    """
    Node can:
    - Own assets (wallet)
    - Make decisions (what to do)
    - Earn money (fees)
    - Spend money (pay for services)
    - Choose strategy (how to maximize)
    
    WITHOUT human intervention
    """
    
    def autonomous_behavior(self):
        # Discover opportunities
        opportunities = self.dht.discover_work()
        
        # Evaluate profitability
        best = max(opportunities, key=lambda o: o.profit)
        
        # Execute if profitable
        if best.profit > self.cost:
            self.do_work(best)
            self.earn(best.payment)
        
        # Invest earnings
        if self.balance > threshold:
            self.invest_in_storage()
            # Or invest in compute
            # Or save for later
        
        # ALL AUTONOMOUS

# Node lies about data
class DishonestNode:
    def serve_data(self, request):
        # Lie about having data
        return fake_data
    
    def what_happens(self):
        # Client verifies merkle proof
        # Proof fails
        # Client doesn't pay
        # Node earns nothing
        
        # No slashing needed
        # Economic incentive sufficient

def bootstrap():
    # Node starts with nothing
    node = AutonomousR3Node()
    
    # Discovers network via DHT
    node.dht.connect()
    
    # Announces availability
    node.dht.announce("I can store/execute")
    
    # Waits for work

def first_earnings():
    # User needs data stored
    user.pays(node, 0.001 ETH)
    
    # Node stores data
    node.bt.store(data)
    
    # Node now has money
    node.balance = 0.001 ETH
    
    # Can reinvest or spend

def strategy():
    if node.balance > 0.01 ETH:
        # Invest in more storage
        node.buy_storage()
        
        # Or invest in compute
        node.buy_compute()
        
        # Or specialize
        node.specialize_in("defi")
    
    # Node decides strategy
    # Based on profit maximization

def adapt():
    # Track what works
    if node.earnings["storage"] > node.earnings["compute"]:
        # Focus on storage
        node.strategy = "storage-focused"
    
    # Learn from market
    # Optimize behavior
    # Maximize profit

# Node A wants to execute contract
class NodeA:
    def execute_contract(self, contract):
        # Need contract state
        state = self.request_state(contract.address)
        
        # Don't have it locally
        if state not in self.cache:
            # Find who has it via DHT
            providers = self.dht.query(contract.address)
            
            # Pick cheapest
            cheapest = min(providers, key=lambda p: p.price)
            
            # Pay them
            self.pay(cheapest, cheapest.price)
            
            # Download state
            state = cheapest.serve(contract.address)
        
        # Execute contract
        result = self.evm.execute(contract, state)
        
        # User pays me
        self.earn(user.fee)

class BootstrapNode:
    def from_zero(self):
        # Start with no money
        self.balance = 0
        
        # Offer cheap service
        self.price = {
            'storage': 0.0001 ETH/GB,
            'bandwidth': 0.00001 ETH/MB
        }
        
        # Compete on price
        # Earn first money
        first_job = self.get_work()
        self.balance = first_job.payment
        
        # Reinvest
        self.buy_more_storage()
        
        # Grow organically
        while True:
            job = self.get_work()
            self.earn(job.payment)
            self.reinvest()

def verify_data(data_hash):
    # Query multiple nodes
    responses = []
    
    for node in dht.query(data_hash):
        response = node.serve(data_hash)
        responses.append(response)
    
    # Verify consistency
    if all_agree(responses):
        return responses[0]  # Trust it
    
    # If conflict
    if conflict(responses):
        # Majority wins
        majority = mode(responses)
        return majority
    
    # Dishonest nodes
    liars = [n for n in responses if n != majority]
    
    # Don't pay them
    for liar in liars:
        blacklist(liar)
    
    # They earn nothing
    # Economic punishment (no slashing)

class AutonomousNode:
    def choose_specialization(self):
        # Analyze market
        market = self.analyze_demand()
        
        # What's most profitable?
        opportunities = {
            'defi_execution': market.defi_profit,
            'nft_storage': market.nft_profit,
            'video_bandwidth': market.video_profit
        }
        
        # Pick best opportunity
        best = max(opportunities, key=opportunities.get)
        
        # Specialize autonomously
        self.specialize(best)
        
        # Invest in specialized hardware
        if best == 'defi_execution':
            self.buy_high_compute()
        elif best == 'nft_storage':
            self.buy_large_storage()
        elif best == 'video_bandwidth':
            self.buy_high_bandwidth()

class EmergentBehavior:
    """
    What emerges from autonomous nodes
    """
    def observe_network(self):
        # Nodes specialize naturally
        specialization = {
            'defi': [node for node in nodes if node.focus == 'defi'],
            'nft': [node for node in nodes if node.focus == 'nft'],
            'video': [node for node in nodes if node.focus == 'video']
        }
        
        # Geographic distribution
        locations = {
            'US': [node for node in nodes if node.location == 'US'],
            'EU': [node for node in nodes if node.location == 'EU'],
            'Asia': [node for node in nodes if node.location == 'Asia']
        }
        
        # Price competition
        prices = sorted([node.price for node in nodes])
        
        # All emergent
        # No central coordination
        # Self-organizing

cd universal-model/
python3 eigennode.py --autonomous

# Node parameters
# --wallet: Generate new wallet
# --strategy: profit-maximizing
# --specialization: auto-detect
# --reinvest: 80%  # Reinvest 80% of earnings

class AutonomousEigenNode(EigenNode):
    def run_autonomously(self):
        while True:
            # Discover opportunities
            work = self.dht.discover_opportunities()
            
            # Evaluate profitability
            profitable = [w for w in work if w.profit > self.cost]
            
            # Execute most profitable
            if profitable:
                best = max(profitable, key=lambda w: w.profit)
                self.execute(best)
                self.earn(best.payment)
            
            # Adapt strategy
            self.optimize_strategy()
            
            # Reinvest earnings
            if self.balance > threshold:
                self.reinvest()

def optimize_strategy(self):
    # Track performance
    if self.earnings['last_hour'] < self.target:
        # Try different strategy
        self.try_new_strategy()
    
    # A/B test approaches
    if random.random() < 0.1:
        self.experiment()
    
    # Learn from others
    successful_nodes = self.dht.query_high_earners()
    self.learn_from(successful_nodes)

class MarketDynamics:
    def price_discovery(self):
        # High demand for DeFi
        if demand['defi'] > supply['defi']:
            # Prices rise
            defi_prices.increase()
            
            # More nodes specialize in DeFi
            # (profit signal)
            new_defi_nodes = [
                node.specialize('defi') 
                for node in nodes 
                if node.detect_opportunity('defi')
            ]
            
            # Supply increases
            supply['defi'] += len(new_defi_nodes)
            
            # Prices stabilize
        
        # Pure market dynamics
        # No central planning

def competition():
    # Node A offers storage at 0.001 ETH/GB
    node_a.price = 0.001
    
    # Node B undercuts
    node_b.price = 0.0009
    
    # Node A loses business
    node_a.customers.decrease()
    
    # Node A adjusts
    node_a.price = 0.0008
    
    # Competition drives efficiency
    # Prices decrease
    # Quality increases

class R3Network:
    """
    Internet of autonomous economic agents
    """
    def __init__(self):
        self.nodes = []  # Start empty
        
    def grow(self):
        # Anyone can join
        new_node = AutonomousNode()
        self.nodes.append(new_node)
        
        # Node earns autonomously
        new_node.start_earning()
        
        # Network grows organically
        # No permission needed
        # No capital required
        # Pure meritocracy
    
    def properties(self):
        return {
            'decentralized': True,
            'permissionless': True,
            'autonomous': True,
            'self-organizing': True,
            'economically_driven': True,
            'scalable': 'Infinite',
            'barrier': '$0'
        }