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aitbc/docs/advanced/05_development/api_reference.md
AITBC System dda703de10 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
2026-03-18 20:17:23 +01:00

2.2 KiB

API Reference - Edge Computing & ML Features

Edge GPU Endpoints

GET /v1/marketplace/edge-gpu/profiles

Get consumer GPU profiles with filtering options.

Query Parameters:

  • architecture (optional): Filter by GPU architecture (turing, ampere, ada_lovelace)
  • edge_optimized (optional): Filter for edge-optimized GPUs
  • min_memory_gb (optional): Minimum memory requirement

Response:

{
  "profiles": [
    {
      "id": "cgp_abc123",
      "gpu_model": "RTX 3060",
      "architecture": "ampere",
      "consumer_grade": true,
      "edge_optimized": true,
      "memory_gb": 12,
      "power_consumption_w": 170,
      "edge_premium_multiplier": 1.0
    }
  ],
  "count": 1
}

POST /v1/marketplace/edge-gpu/scan/{miner_id}

Scan and register edge GPUs for a miner.

Response:

{
  "miner_id": "miner_123",
  "gpus_discovered": 2,
  "gpus_registered": 2,
  "edge_optimized": 1
}

GET /v1/marketplace/edge-gpu/metrics/{gpu_id}

Get real-time edge GPU performance metrics.

Query Parameters:

  • hours (optional): Time range in hours (default: 24)

POST /v1/marketplace/edge-gpu/optimize/inference/{gpu_id}

Optimize ML inference request for edge GPU.

ML ZK Proof Endpoints

POST /v1/ml-zk/prove/inference

Generate ZK proof for ML inference correctness.

Request:

{
  "inputs": {
    "model_id": "model_123",
    "inference_id": "inference_456",
    "expected_output": [2.5]
  },
  "private_inputs": {
    "inputs": [1, 2, 3, 4],
    "weights1": [0.1, 0.2, 0.3, 0.4],
    "biases1": [0.1, 0.2]
  }
}

POST /v1/ml-zk/verify/inference

Verify ZK proof for ML inference.

POST /v1/ml-zk/fhe/inference

Perform ML inference on encrypted data using FHE.

Request:

{
  "scheme": "ckks",
  "provider": "tenseal",
  "input_data": [[1.0, 2.0, 3.0, 4.0]],
  "model": {
    "weights": [[0.1, 0.2, 0.3, 0.4]],
    "biases": [0.5]
  }
}

GET /v1/ml-zk/circuits

List available ML ZK circuits.

Error Codes

Edge GPU Errors

  • 400: Invalid GPU parameters
  • 404: GPU not found
  • 500: GPU discovery failed

ML ZK Errors

  • 400: Invalid proof parameters
  • 404: Circuit not found
  • 500: Proof generation/verification failed