Exploring the intersection of distributed intelligence and computational paradigms. Primary focus on emergent behaviors in multi-agent systems and high-performance computing architectures.

• Multi-agent emergence through stochastic gradient optimization
• Non-convex optimization in high-dimensional parameter spaces
• Asynchronous consensus mechanisms in distributed networks
• Meta-learning architectures for agent adaptation

• CUDA optimization for neural architecture search
• Distributed training on heterogeneous computing clusters
• Memory-efficient transformer implementations
• Edge computing optimization for resource-constrained environments

• Parallel computing paradigms in distributed systems
• Heterogeneous computing infrastructure design
• Low-latency networking for distributed training
• Custom CUDA kernels for specialized neural operations

Developing a framework for autonomous agent coordination in distributed environments. Key components:

• Decentralized decision-making protocols
• Efficient message passing mechanisms
• Dynamic task allocation systems
• Emergent behavior analysis
• Resource optimization under constraints

Core Languages: Python | Rust | Go | JavaScript | C | C++ | Cuda | Solidity

Primary Tools:

Computation: CUDA, OpenCL, MPI
Distributed Systems: Ray, Kubernetes
AI/ML: PyTorch, JAX, Custom CUDA kernels
Edge Computing: TensorRT, ONNX

• Autonomous agent framework for distributed systems in web3
• Custom CUDA kernels for specialized neural operations
• Edge computing optimization toolkit
• ...