Core Problems Solved: View the SMP Repository on GitHub Security Vulnerabilities at Scale Communication Bottlenecks: SMP optimizes efficiency by significantly reducing complexity. This reduces metadata overhead by 700,000x (e.g., shrinking data requirements from 40 TB down to 28 MB for 10 million nodes). Trust and Verification Size: 200-byte proofs Speed: 10ms verification Benefit: Allows for massive updates without the need for re-execution. Data Sovereignty & Privacy Resource Constraints on Edge Devices Specific Application Use Cases Green AI Infrastructure: Moving AI training from power-hungry data centers to a decentralized "edge" network of low-power home devices.   Universal Basic Compute Economy: Allowing node operators to earn rewards for contributing compute power and data without sacrificing ownership.   Private AI Agents: Enabling developers to build secure AI agents using the Python SDK that can learn from personal data locally.