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UltraAImer

UltraAImer is an advanced AI-powered aimbot designed for enhancing precision and accuracy in video games. Built using cutting-edge machine learning algorithms, UltraAImer offers high-speed targeting with exceptional reliability, taking your gaming experience to a whole new level.

Features

  • Dual Detection Modes:
    • YOLO AI-powered detection for precise target identification
    • Color-based detection for lightweight performance
  • Advanced Mouse Movement:
    • Anti-detection movement patterns using bezier curves
    • Hardware mouse modifier support (KmNet/KmBoxB/COM port)
    • Multiple movement options including WinAPI and SendInput
  • Customizable Settings: Offers adjustable parameters for speed, sensitivity, and targeting behavior.
  • Low Latency Performance: Optimized for minimal system impact to ensure smooth gameplay.
  • Multi-Game Support: Compatible with a variety of popular first-person shooter (FPS) games.
  • Security Focused: Built with undetectable code design to avoid anti-cheat systems.

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/UltraAImer.git
    cd UltraAImer
  2. Install dependencies:

    pip install -r requirements.txt
  3. Configure settings:

    • Open config.ini in your preferred text editor

    • Adjust the following parameters as needed:

      Mode Section:

      • aim: Choose targeting method ('Yolo' or 'Color')
      • upper_color: RGB values (160, 200, 255) for color detection range
      • lower_color: RGB values (140, 120, 180) for color detection range

      YOLO Section:

      • model_folder: Directory for model weights (weights)
      • model: YOLO model file name (yolov10n.onnx)
      • img_size: Input image size (320)
      • label_off: Toggle label display (True)
      • label_tab: Label tab settings (1)

      KmNet Section:

      • ip_address: Network IP (192.168.2.188)
      • port: Network port (8737)
      • key: Authentication key (766B5C53)

      Screen Section:

      • screenshot_mode: Screen capture method (dxcam)
      • auto_detection: Enable/disable automatic detection (True)
      • width: Screen width (2560)
      • height: Screen height (1440)

      Mouse Section:

      • offset: Mouse offset (0.25)
      • moving_type: Mouse movement method (KmNet)
      • curve: Movement curve type (beizer)
      • mouse_moving_speed: Movement speed (5)

      COM Section:

      • COM_port: Serial port (COM3)
      • Bauldrate: Communication speed (128000)

      PID Section:

      • kp: Proportional gain (0.6)
      • ki: Integral gain (0.35)
      • kd: Derivative gain (0)

      Key Binds Section:

      • key_reload_config: 0x70
      • key_toggle_aim: 0x72
      • key_toggle_recoil: 0x4F
      • key_exit: 0x73
      • key_trigger: 0x12
      • key_rapid_fire: 0x05
      • aim_keys: 0x06

      Debug Section:

      • enabled: Toggle debug mode (true)
    • Save your changes

  4. Run UltraAImer:

    python main.py

Supported Games

  • Counter-Strike: 2 CS2 YOLO Detection
  • Valorant Valorant YOLO Detection
  • Apex Legends Apex Legends YOLO Detection
  • Overwatch 2

Overwatch 2 YOLO Detection

  • The Finals

The Finals YOLO Detection

  • And many more FPS titles

Disclaimer

UltraAImer is designed for educational and research purposes only. Using aimbots or similar cheating software in online games may violate terms of service and result in account bans. The developers are not responsible for any consequences resulting from the use of this software.

Contributing

We welcome contributions! Please feel free to submit pull requests, report bugs, or suggest new features. Before contributing:

  1. Fork the repository
  2. Create a new branch
  3. Make your changes
  4. Submit a pull request

Contact

For support, feature requests, or general inquiries:

About

Using Yolov10 to create the fastest and most general AI assist for games

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