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Category for free and open-source and proprietary software that runs on various operating systems, that is used to develop video games. Subcategories This category has the following 7 subcategories, out of 7 total.
GameMaker (originally Animo, Game Maker (until 2011) and GameMaker Studio) is a series of cross-platform game engines created by Mark Overmars in 1999 and developed by YoYo Games since 2007. The latest iteration of GameMaker was released in 2022.
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
Download as PDF; Printable version; In other projects ... Listed here are all 494 known games released for the PC-88. [1] List of games Title Release date(s ...
NovelAI is an online cloud-based, SaaS model, and a paid subscription service for AI-assisted storywriting [2] [3] [4] and text-to-image synthesis, [5] originally launched in beta on June 15, 2021, [6] with the image generation feature being implemented later on October 3, 2022.
Squirrel is a high level imperative, object-oriented programming language, designed to be a lightweight scripting language that fits in the size, memory bandwidth, and real-time requirements of applications like video games. MirthKit, a simple toolkit for making and distributing open source, cross-platform 2D games, uses Squirrel for its ...
Game-Maker 3.0, floppy: A three-microfloppy (1.44 MB) package contains the full set of RSD tools, the in-house developed games Tutor, Sample, and Nebula, and three licensed games developed by the independent designer A-J Games: Zark, The Patchwork Heart, and Peach the Lobster. Both packages of version 3.0 include a square-bound 104-page user ...
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.