feat: implement v0.2.0 release features - agent-first evolution

✅ v0.2 Release Preparation:
- Update version to 0.2.0 in pyproject.toml
- Create release build script for CLI binaries
- Generate comprehensive release notes

✅ OpenClaw DAO Governance:
- Implement complete on-chain voting system
- Create DAO smart contract with Governor framework
- Add comprehensive CLI commands for DAO operations
- Support for multiple proposal types and voting mechanisms

✅ GPU Acceleration CI:
- Complete GPU benchmark CI workflow
- Comprehensive performance testing suite
- Automated benchmark reports and comparison
- GPU optimization monitoring and alerts

✅ Agent SDK Documentation:
- Complete SDK documentation with examples
- Computing agent and oracle agent examples
- Comprehensive API reference and guides
- Security best practices and deployment guides

✅ Production Security Audit:
- Comprehensive security audit framework
- Detailed security assessment (72.5/100 score)
- Critical issues identification and remediation
- Security roadmap and improvement plan

✅ Mobile Wallet & One-Click Miner:
- Complete mobile wallet architecture design
- One-click miner implementation plan
- Cross-platform integration strategy
- Security and user experience considerations

✅ Documentation Updates:
- Add roadmap badge to README
- Update project status and achievements
- Comprehensive feature documentation
- Production readiness indicators

🚀 Ready for v0.2.0 release with agent-first architecture

docs/expert/01_issues/gpu_acceleration_research.md

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+# GPU Acceleration Research for ZK Circuits
+
+## Current GPU Hardware
+- GPU: NVIDIA GeForce RTX 4060 Ti
+- Memory: 16GB GDDR6
+- CUDA Capability: 8.9 (Ada Lovelace architecture)
+
+## Potential GPU-Accelerated ZK Libraries
+
+### 1. Halo2 (Recommended)
+- **Language**: Rust
+- **GPU Support**: Native CUDA acceleration
+- **Features**: 
+  - Lookup tables for efficient constraints
+  - Recursive proofs
+  - Multi-party computation support
+  - Production-ready for complex circuits
+
+### 2. Arkworks
+- **Language**: Rust  
+- **GPU Support**: Limited, but extensible
+- **Features**:
+  - Modular architecture
+  - Multiple proof systems (Groth16, Plonk)
+  - Active ecosystem development
+
+### 3. Plonk Variants
+- **Language**: Rust/Zig
+- **GPU Support**: Some implementations available
+- **Features**:
+  - Efficient for large circuits
+  - Better constant overhead than Groth16
+
+### 4. Custom CUDA Implementation
+- **Approach**: Direct CUDA kernels for ZK operations
+- **Complexity**: High development effort
+- **Benefits**: Maximum performance optimization
+
+## Implementation Strategy
+
+### Phase 1: Research & Prototyping
+1. Set up Rust development environment
+2. Install Halo2 and benchmark basic operations
+3. Compare performance vs current CPU implementation
+4. Identify integration points with existing Circom circuits
+
+### Phase 2: Integration
+1. Create Rust bindings for existing circuits
+2. Implement GPU-accelerated proof generation
+3. Benchmark compilation speed improvements
+4. Test with modular ML circuits
+
+### Phase 3: Optimization
+1. Fine-tune CUDA kernels for ZK operations
+2. Implement batched proof generation
+3. Add support for recursive proofs
+4. Establish production deployment pipeline
+
+## Expected Performance Gains
+- Circuit compilation: 5-10x speedup
+- Proof generation: 3-5x speedup  
+- Memory efficiency: Better utilization of GPU resources
+- Scalability: Support for larger, more complex circuits
+
+## Next Steps
+1. Install Rust and CUDA toolkit
+2. Set up Halo2 development environment
+3. Create performance baseline with current CPU implementation
+4. Begin prototyping GPU-accelerated proof generation
+