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## Technologies Used * Objective-C / Objective-C++ * UIKit * Core ML * Vision Framework * CAShapeLayer * MLMultiArray * IOHID Event System * Grand Central Dispatch * Mach Time The main goal of this project is to provide a practical example for developers and researchers interested in computer vision on iOS, including the complete pipeline from image processing and machine learning inference to coordinate transformation, visualization, and input-event research. > This project is intended for educational, research, and experimental purposes to explore the integration of AI and computer vision technologies with iOS systems. Credit thebrazili Share By @Z8TA0

#-Ai-ESP-Aimbot-iOS An experimental and educational iOS project demonstrating how to combine AI-based computer vision with iO
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#-Ai-ESP-Aimbot-iOS An experimental and educational iOS project demonstrating how to combine AI-based computer vision with iOS technologies using Objective-C++, Core ML, and Vision. The project uses a Core ML model to analyze visual frames and detect objects within an image. The detection results are then converted into screen coordinates and rendered visually through a UIKit overlay using CAShapeLayer. ## How It Works 1. Loads a compiled Core ML model from .mlmodelc. 2. Converts the model into a VNCoreMLModel. 3. Captures a visual frame from the application interface. 4. Processes the image through Vision Framework. 5. Reads the model output using MLMultiArray. 6. Parses detection coordinates and confidence values. 7. Converts coordinates from the model's 640×640 coordinate space to screen coordinates. 8. Renders detection results using CAShapeLayer. 9. Demonstrates low-level touch event handling through the IOHID Event System.

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jni.zip9.65 MB

𝗨𝗣𝗗𝗔𝗧𝗘 𝗕𝗟𝗢𝗖𝗞 𝗢𝗙𝗙𝗦𝗘𝗧 𝐁𝐆𝐌𝐈 4.5 32 𝗔𝗡𝗗 64 𝐁𝐈𝐓
32 𝗕𝗜𝗧

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64 𝗕𝗜𝗧

MemoryPatch::createWithHex("libhdmpve.so",0x1F10F0,"C0 03 5F D6").Modify();// update skip
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