92 votes and 26 comments so far on Reddit

During the development of DOOM, John Romero came up with eight special rules to make his levels look and play as good as possible.  This video discusses those rules and shows how they were applied all throughout Knee-Deep in the Dead, DOOM's iconic first episode.

For reference, the eight rules are as follows:
1. Always change floor height when changing floor textures. (0:28)
2. Use border textures between different wall segments and doorways. (1:05)
3. Be strict about texture alignment. (1:47)
4. Use contrast between light and dark areas and cramped and open areas. (2:25)
5. Make sure that if the player can see outside, he can somehow get there. (3:13)
6. Be strict about having several secret areas on each level. (3:53)
7. Make levels flow so that the player revisits areas to better understand the 3D space. (4:44)
8. Create several easily-recognizable landmarks for easier navigation. (5:49)

Original source:
http://web.archive.org/web/20111121052306/https://archive.gamespy.com/articles/december03/doom/romero/…

Will It Ever End? — Original Soundtrack

Interactive Story by Christophe Steam

Music written, produced and mixed by: Ivan Sysoev

iTunes: http://itunes.apple.com/album/id/1342384225
Spotify: http://tinyurl.com/y5wd6fus
Google Play Music: http://tinyurl.com/yybkzhs2
Yandex Music: https://music.yandex.ru/artist/6949922

Watch trailer here: https://youtu.be/sG-qAsKnAFI
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Buy "Will It Eve End?" on AppStore: https://itunes.apple.com/ru/app/will-it-ever-end/id1299534713?mt=8

Дмитрий Куратник, гейм-дизайнер из Glera Games, хочет поделиться с вами советами, как снизить CPI в hyper casual (далее ГК) играх.

Основатели студии FIFTYTWO Михаил Шагин, Дмитрий Викторов и Алесей Калинин расскажут о своем опыте создания игр на платформах Apple. Как три человека из Нижнего Новгорода создали свой международный бизнес? Технологии разработки, дизайн и визуальный стиль, продвижение и работа с аудиторией.

We present a real-time character control mechanism using a novel neural network architecture called a Phase-Functioned Neural Network. In this network structure, the weights are computed via a cyclic function which uses the phase as an input. Along with the phase, our system takes as input user controls, the previous state of the character, the geometry of the scene, and automatically produces high quality motions that achieve the desired user control. The entire network is trained in an end-to-end fashion on a large dataset composed of locomotion such as walking, running, jumping, and climbing movements fitted into virtual environments. Our system can therefore automatically produce motions where the character adapts to different geometric environments such as walking and running over rough terrain, climbing over large rocks, jumping over obstacles, and crouching under low ceilings. Our network architecture produces higher quality results than time-series autoregressive models such as LSTMs as it deals explicitly…

Исходные данные или краткий портрет.

Программа для одной платформы сможет работать и на другой.