This started with Addition Under Pressure, where I gave Claude Code and Codex the same prompt: train the smallest possible transformer that can do 10-digit addition with at least 99% accuracy. Claude Code came back with 6,080 parameters and Codex came back with 1,644. The community has since pushed this dramatically lower.
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:
第一百四十一条 其他法律中规定由公安机关给予行政拘留处罚的，其处罚程序适用本法规定。。服务器推荐是该领域的重要参考
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第二十二条 违反治安管理有下列情形之一的，从重处罚：
Kinesis Advantage 360，推荐阅读safew官方版本下载获取更多信息