AI Aim Assistant Project Looking for exceptional Python Computer Vision engineer to fix my professional AI aim assistance system. This is a technically demanding project requiring expertise in real-time vision systems, ultra-low latency input control, and cross-platform development. To work on multiple different machines ⸻ Technical Requirements You Must Address In your proposal, explain exactly how you'll implement: 1. **YOLO v5-v11 Integration** - Dynamic model switching system - Real-time GPU optimization with CPU fallback - Memory-efficient inference pipeline 2. **1000Hz Input Control** - Sub-millisecond controller input - DirectInput/vJoy implementation - Vigembuss - Anti-recoil compensation algorithms 3. **Cross-Platform Support** - High-performance screen capture - Platform-specific input handling - Remote play integration (Chiaki, Steam Link) 4. **Advanced PID Control** - Smooth predictive aiming - Anti-recoil pattern learning - Rapid-fire modulation 5. **Professional UI** - Real-time Pyaide6 overlay already made! - Interactive 3D body targeting - Live performance monitoring ⸻ Required Expertise Essential Skills: • Advanced Python with Computer Vision focus • YOLO model deployment and optimization • Real-time systems (<50ms latency) • DirectInput and controller programming • Cross-platform development • PySide6 interface design Preferred: • Gaming industry experience • Machine learning optimization • Remote desktop protocols ⸻ Project Requirements Phase 1: Core Architecture • YOLO model abstraction layer • Real-time screen capture system • 1000Hz input controller framework • Cross-platform compatibility layer • Performance monitoring system Phase 2: Advanced Features • PID controller with anti-recoil • Rapid-fire modulation system • Remote play compatibility • GPU optimization (TensorRT/ONNX) • Security/anti-detection features Phase 3: UI & Integration • Modern PyQt6 application • Real-time overlay system • 3D body diagram selector • Settings configuration panels • Performance monitoring dashboard ⸻ Game Compatibility Requirements Technical Integration: • Window detection and targeting • Game-specific recoil patterns • Resolution and FOV scaling • Custom config profiles per game ⸻ Selection Criteria Your proposal MUST include: 1. **Technical Implementation Plan** - Detailed architecture explanation - Specific libraries and frameworks - Performance optimization strategies - Cross-platform compatibility approach 2. **Portfolio Evidence** - Links to similar Computer Vision projects - Performance benchmarks from previous work - Code samples demonstrating relevant skills - GitHub repository with professional projects 3. **Development Approach** - Methodology for testing and validation - How you'll achieve 1000Hz input response - GPU optimization techniques for YOLO models - Anti-detection implementation strategy ⸻ Auto-Rejection Criteria We will NOT consider proposals that: • Use generic templates without specific technical details • Lack experience with real-time computer vision systems • Cannot demonstrate YOLO model integration experience • Don't address the 1000Hz input requirement specifically • Fail to explain cross-platform compatibility approach • Skip anti-recoil and rapid-fire implementation details • Don't include portfolio evidence of similar projects ⸻ What We're Looking For The ideal candidate will: • Have built real-time Computer Vision applications before • Understand gaming input systems and latency requirements • Be able to optimize YOLO models for GPU inference • Experience with DirectInput and controller programming • Deliver professional-grade code with proper documentation • Communicate technical concepts clearly and precisely