Search Results for "cheat engine 6.0"
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Showing 5 open source projects for "cheat engine 6.0"
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1
NiL.JS
JavaScript engine for .NET written in C#
NiL.JS is a JavaScript engine written in C#, capable of executing ECMAScript 6.0 (ES2015) code. It allows embedding JavaScript execution within .NET applications, providing a bridge between C# and JavaScript. -
2
Quake3e
Improved Quake III Arena engine
Quake3e is a modernized fork of id Software’s Quake III Arena engine, tailored for mod developers and competitive players. It includes numerous technical improvements while preserving the core gameplay and mod compatibility of the original. Focused on performance, security, and extended modding capabilities, it has become a popular base for competitive tournaments and engine forks. With its updated rendering path and support for high refresh rates, Quake3e offers a slick and responsive... -
3
ILSpy
.NET Decompiler with support for PDB generation, ReadyToRun, Metadata
...Visual Studio 16.3 and later include a version of the .NET (Core) SDK that is managed by the Visual Studio installer - once you update, it may get upgraded too. Please note that ILSpy is only compatible with the .NET 6.0 SDK and Visual Studio will refuse to load some projects in the solution. -
4
FerretDB
A truly Open Source MongoDB alternative
MongoDB is a life-changing technology for many developers, empowering them to build applications faster than using relational databases. However, MongoDB abandoned its Open-Source roots, changing the license to SSPL making it unusable for many Open Source and Commercial Projects. FerretDB is going to be a perfect solution for those looking for MongoDB development experience available as fully Open Source Software. The core of our solution is a stateless proxy, which converts MongoDB protocol... -
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5
Genetic Algorithms Engine - Blackjack
A genetic algortihm engine that evolves blackjack basic strategy.
...The code was written in C++, using MS Visual Studio 6.0 and MS Visual Source Safe 6.0. The genetic algorithm engine supports various mutation rates, ranked parental selection, stochastic sampling parental selection, cyclic crossover, crossover at each gene, cloning the best individual each generation, and creating random individuals each generation. To use the genetic algorithm engine to search for a different problem's solution, one needs to program a fitness function, the project settings, and a few virtual functions.
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