32 entries may sound small by modern standards (current x86 processors have thousands of TLB entries), but it covers 128 KB of memory -- enough for the working set of most 1980s programs. A TLB miss is not catastrophic either; the hardware page walker handles it transparently in about 20 cycles.
Lightning sparked 60% of Canada’s catastrophic 2023 wildfires, which burned tens of millions of acres and forced hundreds of thousands to evacuate. Skyward initially claimed on its website that it could prevent “up to 100% of lightning strikes,” but quietly removed the statement after MIT Technology Review began asking questions.
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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: