How Pythia Works
A topological super intelligence that optimizes compute allocation in real-time for maximum collective benefit.
The Decision Engine
Every moment, Pythia evaluates network conditions and decides the optimal allocation of compute resources across the mesh.
Sense
Pythia continuously monitors network state: task queue depth, Bitcoin difficulty, energy costs, and node availability across all tiers.
Predict
Using Active Inference, Pythia generates predictions about future states and calculates expected value for each allocation strategy.
Act
The action that minimizes free energy (surprise) is selected. Compute is allocated to tasks or Bitcoin mining based on optimal returns.
Active Inference
Built on Karl Friston's Free Energy Principle, Active Inference is a framework from computational neuroscience that describes how intelligent agents minimize surprise in their environment.
- ●Generative Model: Pythia maintains a model of how the network behaves and uses it to predict outcomes.
- ●Belief Updating: As new data arrives, beliefs are updated using Bayesian inference.
- ●Policy Selection: Actions are chosen to minimize expected free energy (surprise + uncertainty).
// Simplified Active Inference Loop
while network.is_running() {
// 1. Observe current state
state = sense_network();
// 2. Update beliefs
beliefs = update_beliefs(
prior_beliefs,
state
);
// 3. Evaluate policies
policies = [
allocate_to_tasks(beliefs),
allocate_to_mining(beliefs),
hybrid_allocation(beliefs)
];
// 4. Select action minimizing
// expected free energy
action = argmin(
policies.map(p =>
expected_free_energy(p, beliefs)
)
);
// 5. Execute and learn
execute(action);
}Economic Optimization
Pythia continuously evaluates whether to execute user tasks or mine Bitcoin, always choosing the option that maximizes value for the collective.
Task Execution Mode
When the task queue is full and prices are favorable:
- • AI inference workloads
- • Data processing jobs
- • Model training (federated)
- • Custom compute tasks
Bitcoin Mining Mode
When task demand is low or mining is more profitable:
- • Pooled mining operations
- • Stratum v2 protocol
- • Efficient power management
- • Heat recycling where possible
Federated Learning
Pythia learns from the entire network without ever seeing raw data. Each device trains locally; only encrypted gradients are shared.
- ✓Raw data never leaves your device
- ✓Differential privacy protects individual contributions
- ✓The collective model improves for everyone
Explore the Technology Stack
Dive deeper into the cryptographic protocols, network topology, and system architecture that powers Pythia AI.