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Breaking the rules of video game design with a collection of free strategy games
4 points by scorecard 5 days ago | hide | past | web | favorite | 4 comments
Design principles for the eSports for engineers game collection are:

1. target audience is people who enjoy reading dense technical material

2. designed to accompany your daily exercise bike workout, for 30 min to one hour a day total. The collection includes a different game for each day, providing for a varied mind/body workout. Scoring formulas are provided to track your daily and weekly performance.

3. Sample different game domains, from deep learning chess and Go to realistic sims. Spar with a world-champion level AI first to train, then play online vs other humans if desired.

4. realistic physics, re-creation of historical events and strategy, not fantasy, are the themes that draw you in.

5. free on linux, no advertising.

6. you'll need to read a book and a game manual for each game to get the most out it

7. differentiate with breadth, depth, and richness of content, reflecting a wide range of volunteer contributors from all over the world, and over a long period of time.

8. unpack one tar file on linux to play all the games. No payment infrastructure or internet is needed to play.

more information here:

https://alternativeto.net/software/esports-for-engineers/






Free, world champion Chess, Go and Bridge were a big find, and were added for version 3. All functionality of Mig Alley's well-known unscripted campaign has been restored. The daily and weekly score mechanism has been added. And last but not least, QA has been done to improve the user experience.

What's new in the 3.0 release of esports for engineers?

Does this game package run on Manjaro linux?

The recommended linux distro for eSports for engineers is Ubuntu 20.04 LTS, and wine 5.0 or higher. That way you just follow the instructions in the documentation and everything runs with no hassle.

Most of the games will probably run out-of-the-box on any linux distro, but the sims and GPU-accelerated deep learning engine might require some additional work loading needed libraries, etc.




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