base on World's Best AI Aimbot - CS2, Valorant, Fortnite, APEX, every game # 🎯 World's Best AI Aimbot 🎮  [](https://makeapullrequest.com) Want to make your own bot? Then use the [Starter Code Pack](https://github.com/RootKit-Org/AI-Aimbot-Starter-Code)! -- -- ## 🙌 Welcome Aboard! We're a charity on a mission to educate and certify the upcoming wave of developers in the world of Computer Engineering 🌍. Need assistance? Hop into our [Discord](https://discord.gg/rootkitorg) and toss your questions at `@Wonder` in the *#ai-aimbot channel* (be sure to stick to this channel or face the consequences! 😬). Type away your query and include `@Wonder` in there. Our *AI Aimbot* 🤖 sharpshoots targets in **any game with humanoid characters**, harnessing the power of [YOLOv5](https://github.com/ultralytics). Currently, it's a ninja against anti-cheat systems, as it's visual-only. Still, watch out for manual player reports! 👀 Intended for educational use 🎓, our aim is to highlight the vulnerability of game devs to AI-driven cheats. Pass it along to your game developer buddies, and save their games from being outsmarted! **⚠ Use at your own risk! If you're caught... well, you've been warned!** ## 📹 Instructional Media - [Watch the tutorial video (Works But Outdated)](https://www.youtube.com/watch?v=TCJHLbbeLhg) - [Watch the live stream explainer (Works But Outdated)](https://www.youtube.com/watch?v=uniL5yR7y0M&ab_channel=RootKit) - [Join the Discord](https://discord.gg/rootkitorg) ## There are 3 Versions 🚀🚦🖥️ - Fast 🏃‍♂️ - `main.py` ✅ Easy to set up, Works on any computer 💻 - Faster 🏃‍♂️💨 - `main_onnx.py` ⚙️ May need to edit a file, Works on any computer 💻 - Fastest 🚀 - `main_tensorrt.py` 🏢 Enterprise level hard, Works on computers with Nvidia GPUs only 🎮 ## 🧰 Requirements - Nvidia RTX 980 🆙, higher or equivalent - And one of the following: - Nvidia CUDA Toolkit 11.8 [DOWNLOAD HERE](https://developer.nvidia.com/cuda-11-8-0-download-archive) ## 🚀 Pre-setup Steps 1. Download and Unzip the AI Aimbot and stash the folder somewhere handy 🗂️. 2. Ensure you've got Python installed (like a pet python 🐍) – grab version 3.11 [HERE](https://www.python.org/downloads/release/python-3116/). - 🛑 Facing a `python is not recognized...` error? [WATCH THIS!](https://youtu.be/E2HvWhhAW0g) - 🛑 Is it a `pip is not recognized...` error? [WATCH THIS!](https://youtu.be/zWYvRS7DtOg) 3. Fire up `PowerShell` or `Command Prompt` on Windows 🔍. 4. To install `PyTorch`, select the appropriate command based on your GPU. - Nvidia `pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118` - AMD or CPU `pip install torch torchvision torchaudio` 5. 📦 Run the command below to install the required Open Source packages: ``` pip install -r requirements.txt ``` ## 🔌 How to Run (Fast 🏃‍♂️ Version) Follow these steps **after** Python and all packages have been installed: 1. Open `PowerShell` ⚡ or `Command Prompt` 💻. 2. Input `cd `, then drag & drop the folder containing the bot code into the terminal. 3. Hit Enter ↩️. 4. Type `python main.py` and press Enter. 5. Use **CAPS_LOCK** to toggle the aimbot 🎯. It begins in the *off* state. 6. Pressing `q` 💣 at **ANY TIME** will shut down the program. ## 🔌 How to Run (Faster 🏃‍♂️💨 Version) Follow these steps **after** Python and all packages have been installed: 1. Open the `config.py` 📄 file and tweak the `onnxChoice` variable to correspond with your hardware specs: - `onnxChoice = 1` # CPU ONLY 🖥 - `onnxChoice = 2` # AMD/NVIDIA ONLY 🎮 - `onnxChoice = 3` # NVIDIA ONLY 🏎️ 2. IF you have an NVIDIA set up, run the following ``` pip install onnxruntime-gpu pip install cupy-cuda11x ``` 2. Follow the same steps as for the Fast 🏃‍♂️ Version above except for step 4, you will run `python main_onnx.py` instead. ## 🔌 How to Run (Fastest 🚀 Version) Follow these sparkly steps to get your TensorRT ready for action! 🛠️✨ 1. **Introduction** 🎬 Watch the TensorRT section of the setup [video 🎥](https://www.youtube.com/watch?v=uniL5yR7y0M&ab_channel=RootKit) before you begin. It's loaded with useful tips! 2. **Oops! Don't Forget the Environment** 🌱 We forgot to mention adding environmental variable paths in the video. Make sure to do this part! 3. **Get Support If You're Stumped** 🤔 If you ever feel lost, you can always `@Wonder` your questions in our [Discord 💬](https://discord.gg/rootkitorg). Wonder is here to help! 4. **Install Cupy** Run the following `pip install cupy-cuda11x` 5. **CUDNN Installation** 🧩 Click to install [CUDNN 📥](https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.6/local_installers/11.x/cudnn-windows-x86_64-8.9.6.50_cuda11-archive.zip/). You'll need a Nvidia account to proceed. Don't worry it's free. 6. **Unzip and Relocate** 📁➡️ Open the .zip CuDNN file and move all the folders/files to where the CUDA Toolkit is on your machine, usually at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8`. 7. **Get TensorRT 8.6 GA** 🔽 Fetch [`TensorRT 8.6 GA 🛒`](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/zip/TensorRT-8.6.1.6.Windows10.x86_64.cuda-11.8.zip). 8. **Unzip and Relocate** 📁➡️ Open the .zip TensorRT file and move all the folders/files to where the CUDA Toolkit is on your machine, usually at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8`. 9. **Python TensorRT Installation** 🎡 Once you have all the files copied over, you should have a folder at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\python`. If you do, good, then run the following command to install TensorRT in python. ``` pip install "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\python\tensorrt-8.6.1-cp311-none-win_amd64.whl" ``` 🚨 If the following steps didn't work, don't stress out! 😅 The labeling of the files corresponds with the Python version you have installed on your machine. We're not looking for the 'lean' or 'dispatch' versions. 🔍 Just locate the correct file and replace the path with your new one. 🔄 You've got this! 💪 10. **Set Your Environmental Variables** 🌎 Add these paths to your environment: - `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib` - `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\libnvvp` - `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin` 11. **Download Pre-trained Models** 🤖 You can use one of the .engine models we supply. But if it doesn't work, then you will need to re-export it. Grab the `.pt` file here for the model you want. We recommend `yolov5s.py` or `yolov5m.py` [HERE 🔗](https://github.com/ultralytics/yolov5/releases/tag/v7.0). 12. **Run the Export Script** 🏃‍♂️💻 Time to execute `export.py` with the following command. Patience is key; it might look frozen, but it's just concentrating hard! Can take up to 20 minutes. ``` python .\export.py --weights ./yolov5s.pt --include engine --half --imgsz 320 320 --device 0 ``` Note: You can pick a different YOLOv5 model size. TensorRT's power allows for larger models if desired! If you've followed these steps, you should be all set with TensorRT! ⚙️🚀 ## ⚙️ Configurable Settings *Default settings are generally great for most scenarios. Check out the comments in the code for more insights. 🔍 The configuration settings are now located in the `config.py` file!<br> **CAPS_LOCK is the default for flipping the switch on the autoaim superpower! ⚙️ 🎯** `useMask` - Set to `True` or `False` to turn on and off 🎭 `maskWidth` - The width of the mask to use. Only used when `useMask` is `True` 📐 `maskHeight` - The height of the mask to use. Only used when `useMask` is `True` 📐 `aaQuitKey` - The go-to key is `q`, but if it clashes with your game style, swap it out! ⌨️♻️ `headshot_mode` - Set to `False` if you're aiming to keep things less head-on and more centered. 🎯➡️👕 `cpsDisplay` - Toggle off with `False` if you prefer not to display the CPS in your command station. 💻🚫 `visuals` - Flip to `True` to witness the AI's vision! Great for sleuthing out any hiccups. 🕵️‍♂️✅ `aaMovementAmp` - The preset should be on point for 99% of players. Lower the digits for smoother targeting. Recommended doses: `0.5` - `2`. ⚖️🕹️ `confidence` - Stick with the script here unless you're the expert. 🧐✨ `screenShotHeight` - Same as above, no need for changes unless you've got a specific vision. 📏🖼️ `screenShotWidth` - Keep it constant as is, unless you've got reasons to adjust. 📐🖼️ `aaDetectionBox` - Default's your best bet, change only if you've got the know-how. 📦✅ `onnxChoice` - Gear up for the right graphics card—Nvidia, AMD, or CPU power! 💻👾 `centerOfScreen` - Keep this switched on to stay in the game's heart. ❤️🖥️ ## 📊 Current Stats The bot's efficiency depends on your setup. We achieved 100-150 CPS with our test specs below 🚀. - AMD Ryzen 7 2700 - 64 GB DDR4 - Nvidia RTX 3080 💡 Tip: Machine Learning can be tricky, so reboot if you keep hitting CUDA walls. ## 🤝 Community Based We're all about collaboration. Your contributions can earn you credit and potential 💰! **Want to volunteer? Have video or program ideas? Tell us!** ## ⚠️ Known Cheat-Detectable Games Splitgate (reported by a Discord user 🕵️‍♂️), EQU8 detects win32 mouse movement library. ## 🚀 Custom Aimbots and Models Show off your work or new models via Pull Requests in `customScripts` or `customModels` directories, respectively. Check out the `example-user` folder for guidance. ## 🌠 Future Ideas - [x] Mask Player to avoid false positives Happy Coding and Aiming! 🎉👾 ", Assign "at most 3 tags" to the expected json: {"id":"6496","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"