Files

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

1.5 KiB

Raw Blame History

Automatic Topology Selection

Purpose

Automatically select the optimal swarm topology based on task complexity analysis.

How It Works

1. Task Analysis

The system analyzes your task description to determine:

Complexity level (simple/medium/complex)
Required agent types
Estimated duration
Resource requirements

2. Topology Selection

Based on analysis, it selects:

Star: For simple, centralized tasks
Mesh: For medium complexity with flexibility needs
Hierarchical: For complex tasks requiring structure
Ring: For sequential processing workflows

3. Example Usage

Simple Task:

Tool: mcp__claude-flow__task_orchestrate
Parameters: {"task": "Fix typo in README.md"}
Result: Automatically uses star topology with single agent

Complex Task:

Tool: mcp__claude-flow__task_orchestrate
Parameters: {"task": "Refactor authentication system with JWT, add tests, update documentation"}
Result: Automatically uses hierarchical topology with architect, coder, and tester agents

Benefits

🎯 Optimal performance for each task type
🤖 Automatic agent assignment
⚡ Reduced setup time
📊 Better resource utilization

Hook Configuration

The pre-task hook automatically handles topology selection:

{
  "command": "npx claude-flow hook pre-task --optimize-topology"
}

Direct Optimization

Tool: mcp__claude-flow__topology_optimize
Parameters: {"swarmId": "current"}

CLI Usage

# Auto-optimize topology via CLI
npx claude-flow optimize topology

1.5 KiB Raw Blame History