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Add archived descriptions for 15 repositories
Co-authored-by: gmh5225.eth <gmh5225@users.noreply.github.com>
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This project is an experimental dynamic binary instrumentation framework for x86-64 binaries on Windows. It is written in C++ and organized with a loader, an instrumentation runtime, and a dedicated test target for validation. The codebase includes components for translation, rewriting, memory handling, and exception processing, with dependencies on Zydis and AsmJIT. It is mainly useful for reverse engineering, runtime analysis, and low-level binary research.
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This project is a lightweight multiplayer Snake game prototype built in a short development sprint. It is implemented in C++ with a custom networking layer and supports hosting or joining matches from the command line. The source includes game logic, serialization, socket handling, and optional headless server hosting for simple deployment. It is primarily aimed at learning and experimenting with real-time multiplayer game programming rather than security research.
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This project is an x64dbg plugin that adds Lua scripting support to the debugger. It combines C/C++ plugin code with an embedded Lua runtime and bundled Lua libraries to automate debugger workflows. The plugin exposes functionality around memory, registers, breakpoints, labels, modules, and assembler operations, and also supports autorun scripts and examples. It is useful for reverse engineers and game security researchers who want scriptable debugging and analysis.
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This project is a work-in-progress x86-64 user-mode emulator that uses JIT-generated handlers instead of a purely interpreted execution model. It is written in modern C++ and relies on Zydis for instruction decoding and encoding support. The repository includes emulator core components such as code generation, code caching, CPU and memory logic, plus playground and test targets. It is intended for low-level emulation, binary analysis, and advanced reverse engineering research.
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This project is a C++ internal cheat framework targeting the game Rust. It includes gameplay modules for visuals, hooks, math, weapon logic, and Unity/IL2CPP data access, plus an ImGui-based in-game menu. The codebase also contains anti-cheat related bypass utilities, detours, pattern scanning, and spoofed call helpers implemented with C++ and assembly. It is mainly aimed at cheat development and anti-cheat bypass experimentation.
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This project is a C++ internal Rust cheat source with rendering, hook management, and gameplay feature modules. It provides components for ESP-style visuals, internal logic hooks, Unity/IL2CPP structure access, and configurable menu systems built with ImGui. The repository also includes bypass-oriented utilities such as detours, import/scan helpers, and spoofcall routines for low-level call handling. It is primarily intended for game cheat prototyping and reverse engineering practice.
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This project is a Godot-based recreation effort for MapleStory-style client gameplay. The client is written in C# using Godot .NET and is accompanied by a large amount of scene, sprite, and audio assets for character and map presentation. Its structure focuses on client-side content organization and learning-oriented implementation rather than production deployment. It is mainly useful for developers studying 2D MMORPG client architecture and content workflows.
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This project is a small collection of external Fortnite exploit code snippets. It uses C++ examples that demonstrate direct memory writes for behaviors such as position manipulation and simple spin-style logic. The repository is minimal and focuses on raw proof-of-concept fragments rather than a complete framework. It is primarily aimed at quick cheat experimentation and learning external memory manipulation patterns.
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This project is a C++ proof of concept for external world-to-screen calculations in Fortnite. It includes code to read and decrypt camera/viewpoint data from process memory and convert 3D world positions into 2D screen coordinates. The implementation demonstrates matrix construction, axis transformation, and perspective projection math used by ESP overlays. It is mainly useful as a foundation for external visualization tooling in game hacking research.
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This project is a compact Fortnite offsets and signatures reference with supporting C++ snippets. It contains example pattern-scanning code, offset notes for key globals, and helper logic for address derivation and string decryption workflows. The focus is on fast offset maintenance and signature-based relocation after game updates. It is intended for reverse engineers and cheat developers maintaining external or internal tooling.
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This project is an Arduino-based automation bot for Escape from Tarkov training routines. It uses HID-capable boards to emulate keyboard and mouse input so repetitive movement cycles can be executed automatically in-game. The repository includes multiple .ino sketches plus a Python coordinate conversion helper for adapting button positions and flow updates. It is mainly targeted at hardware-assisted game automation experiments and skill-grinding scripts.
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This project is a Windows C++ client/server toolkit for process memory interaction over named pipes. The server side exposes operations like reading and writing memory, changing protections, allocating remote memory, and querying module bases, while the client issues structured requests. Utility code inspects system handles and process access state to acquire usable handles before performing memory operations. It is primarily used for low-level game hacking research and handle-based anti-cheat evasion experiments.
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This project demonstrates low-level mouse event injection through the undocumented NtUserInjectMouseInput syscall. It is implemented in C++ with custom input structures and a small interface layer for movement and click actions. The approach provides an alternative to higher-level input simulation APIs and highlights Windows internals relevant to input paths. It is mainly useful for automation research, input emulation tooling, and cheat-adjacent experimentation.
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This project is a Python-based Apex Legends cheat that uses YOLOv5 computer vision models to detect enemies from screen data. It includes trained model weights, configurable runtime parameters, overlay and aiming features, and update/version control logic. The design emphasizes AI-driven detection and control instead of direct process memory hooking, with scripts for both Windows and Linux-style workflows. It is primarily aimed at machine-learning-assisted game automation and anti-cheat-aware experimentation.
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This project is an open-source anti-analysis testing tool that emulates detection techniques used by real malware. It is written in C and includes modules that check for virtual machines, sandboxes, debuggers, hooks, and environment artifacts across platforms such as VMware, VirtualBox, QEMU, and Wine. The code is intended for reproducible testing and can be built with MinGW-w64 and make-based workflows. It is mainly used by security researchers to evaluate analysis environments and study evasion behavior.
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