AimBuddy

AimBuddy is an AI-based Android aim assistant for real-time screen capture, object detection, target tracking, visual guidance overlays, and optional assisted input.

Project in Brief

• Platform: Android (arm64-v8a)
• App stack: Kotlin + Jetpack Compose + native C++ (JNI)
• Inference: NCNN runtime with Vulkan GPU acceleration (YOLOv26n)
• Training: Python pipeline with Ultralytics, PyTorch, and NCNN export

Runtime Modes

Mode	Root Required	Features
Visual Assist	No	Screen capture, YOLO inference, target tracking, ESP overlays
Assisted Input	Yes	Everything above + low-latency touch injection via uinput

If root is unavailable, the app remains fully functional in Visual Assist Mode.

How It Works

flowchart LR
    A[Screen Capture] --> B[Frame Buffer]
    B --> C[YOLO Inference]
    C --> D[Target Tracker]
    D --> E[Aim Controller]
    E --> F[Touch Injection]
    C --> G[ESP Overlay]
    D --> G

Loading

1. Capture: MediaProjection captures the game screen at 1280x720.
2. Detect: YOLOv26n runs on the GPU via NCNN to detect enemies.
3. Track: DeepSORT-style tracker maintains target identity across frames.
4. Aim: PD controller steers aim with velocity lead and jitter suppression.
5. Render: ESP overlay draws boxes, snap lines, and an ImGui settings menu.

Device Requirements

Runtime (Android Device)

Item	Minimum	Recommended
Android	11 (API 30)	13+
ABI	arm64-v8a	Snapdragon 888+
Graphics	OpenGL ES 3.1	OpenGL ES 3.2 + Vulkan
RAM	6 GB	8 GB+
Storage	2 GB free	5 GB+ free
Root	Not required for ESP	Required for aim assist

Training (Windows PC)

Item	Minimum	Recommended
OS	Windows 10 64-bit	Windows 11 64-bit
Python	3.10 to 3.12	3.11
CPU	4 cores	8+ cores
RAM	8 GB	16 GB+
Storage	15 GB free	30 GB+ free
GPU	CPU-only supported	NVIDIA with CUDA 12.1

Quick Start

1. Build and Install

./gradlew.bat clean assembleDebug
./gradlew.bat installDebug

Prerequisites:

• Android SDK 35
• Android NDK 29.0.13113456 rc1
• CMake 3.22.1
• Java 11+

2. First Launch

1. Open AimBuddy on your device.
2. Grant overlay permission when prompted.
3. Approve or deny root access (aim assist requires root).
4. Grant MediaProjection screen capture permission.
5. The overlay appears with ESP boxes and a floating settings icon.

3. Train Your Own Model (Optional)

cd training
scripts\07_run_full_pipeline.bat

See the Training Guide for dataset setup and individual scripts.

Build Details

Debug Build

./gradlew.bat clean assembleDebug

Release Build

./gradlew.bat clean assembleRelease

Manual APK Install

adb install -r app/build/outputs/apk/debug/app-debug.apk

Training and Export

From the repository root:

cd training
scripts\07_run_full_pipeline.bat

This runs environment setup, dataset validation, training, and NCNN export.

Key outputs:

Output	Location
Reports	training/outputs/reports/
Weights	training/outputs/runs/detect/train/weights/
NCNN export	training/outputs/export/
Deployment target	app/src/main/assets/models/

Individual scripts:

scripts\01_setup_environment.bat
scripts\02_extract_frames.bat
scripts\03_validate_dataset.bat
scripts\04_train_adaptive.bat
scripts\05_train_manual.bat
scripts\06_export_ncnn.bat

Documentation

Document	Contents
Architecture	System design, threading, data flow, module reference
Settings Guide	Every setting explained, presets, tuning workflow
Performance	Pipeline targets, adaptive crop, telemetry, optimization
Training	Dataset format, training scripts, NCNN export
Troubleshooting	Build, runtime, and training issue resolution
Contributing	Code standards, PR process, validation requirements

Repository Layout

app/                    Android app and native runtime
    src/main/
        java/           Kotlin sources (MainActivity, services)
        cpp/            C++ native code
            aimbot/     Target tracker, aim controller
            detector/   YOLO inference (NCNN)
            input/      Touch injection (uinput)
            renderer/   ESP overlay, ImGui menu
            utils/      Settings, math, logging
        assets/models/  NCNN model files
training/               Python training pipeline
    scripts/            Batch scripts for each step
    config/             Training configuration
    dataset/            Training data (not committed)
    outputs/            Reports, weights, exports
docs/                   Technical documentation

External Credits

Android App and Runtime

• AndroidX Core/AppCompat
• Material Components for Android
• Jetpack Compose
• AndroidSVG
• NCNN
• Dear ImGui

Training and Export

• Ultralytics
• PyTorch
• TorchVision
• OpenCV
• NumPy
• ONNX
• ONNX Runtime

See training/requirements.txt and app/build.gradle for exact dependency versions.

License

This project is released under the AimBuddy Community Free Use License v1.0.

Key terms:

• Free use, modification, and redistribution are allowed.
• Selling or commercial monetization is not allowed.
• Derivative works must remain free and use the same license.
• Attribution to the original project is required.
• Software is provided as-is with no warranty and no liability.

See LICENSE for full terms.

Legal and Usage Notice

Use this project only in authorized environments and only where local law, platform policy, and software terms allow such testing.