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The 386 solves this by repurposing RPT (Repeat). Normally, RPT implements loops -- it re-executes a micro-instruction while decrementing a counter, as we saw in the multiplication post. But when a protection test is in flight, the hardware suppresses RPT's counter-decrement and turns it into a pure stall: the sequencer freezes until the PLA result arrives.
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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: