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Claude 6ed69a3d48 feat: Complete Rust port of WiFi-DensePose with modular crates

Major changes:
- Organized Python v1 implementation into v1/ subdirectory
- Created Rust workspace with 9 modular crates:
  - wifi-densepose-core: Core types, traits, errors
  - wifi-densepose-signal: CSI processing, phase sanitization, FFT
  - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch)
  - wifi-densepose-api: Axum-based REST/WebSocket API
  - wifi-densepose-db: SQLx database layer
  - wifi-densepose-config: Configuration management
  - wifi-densepose-hardware: Hardware abstraction
  - wifi-densepose-wasm: WebAssembly bindings
  - wifi-densepose-cli: Command-line interface

Documentation:
- ADR-001: Workspace structure
- ADR-002: Signal processing library selection
- ADR-003: Neural network inference strategy
- DDD domain model with bounded contexts

Testing:
- 69 tests passing across all crates
- Signal processing: 45 tests
- Neural networks: 21 tests
- Core: 3 doc tests

Performance targets:
- 10x faster CSI processing (~0.5ms vs ~5ms)
- 5x lower memory usage (~100MB vs ~500MB)
- WASM support for browser deployment

2026-01-13 03:11:16 +00:00

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Performance Bottleneck Analysis

Purpose

Identify and resolve performance bottlenecks in your development workflow.

Automated Analysis

1. Real-time Detection

The post-task hook automatically analyzes:

Execution time vs. complexity
Agent utilization rates
Resource constraints
Operation patterns

2. Common Bottlenecks

Time Bottlenecks:

Tasks taking > 5 minutes
Sequential operations that could parallelize
Redundant file operations

Coordination Bottlenecks:

Single agent for complex tasks
Unbalanced agent workloads
Poor topology selection

Resource Bottlenecks:

High operation count (> 100)
Memory constraints
I/O limitations

3. Improvement Suggestions

Tool: mcp__claude-flow__task_results
Parameters: {"taskId": "task-123", "format": "detailed"}

Result includes:
{
  "bottlenecks": [
    {
      "type": "coordination",
      "severity": "high",
      "description": "Single agent used for complex task",
      "recommendation": "Spawn specialized agents for parallel work"
    }
  ],
  "improvements": [
    {
      "area": "execution_time",
      "suggestion": "Use parallel task execution",
      "expectedImprovement": "30-50% time reduction"
    }
  ]
}

Continuous Optimization

The system learns from each task to prevent future bottlenecks!

1.3 KiB Raw Blame History