Meadhainnigh, a college-aged chemical engineering student, first learned about Tamriel Rebuilt through the promotional video for Grasping Fortune, the project’s most recent update. The roughly three-minute video showcases some of the landscapes, cityscapes, and interiors, the culmination of thousands of hours of work. LogansGun is credited as the creator of that video, which has been used to inspire the next wave of contributors.
“我们需要的是立足于实际又胸怀长远目标的实干，而不需要不甘寂寞、好高骛远的空想；我们需要的是一步一个脚印的实干精神，而不需要新官上任只烧三把火希图侥幸成功的投机心理；我们需要的是锲而不舍的韧劲，而不需要‘三天打鱼，两天晒网’的散漫。”
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Can these agent-benchmaxxed implementations actually beat the existing machine learning algorithm libraries, despite those libraries already being written in a low-level language such as C/C++/Fortran? Here are the results on my personal MacBook Pro comparing the CPU benchmarks of the Rust implementations of various computationally intensive ML algorithms to their respective popular implementations, where the agentic Rust results are within similarity tolerance with the battle-tested implementations and Python packages are compared against the Python bindings of the agent-coded Rust packages:
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