Streaming server-side rendering (SSR) is a particularly painful case. A typical SSR stream might render thousands of small HTML fragments, each passing through the streams machinery:
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Sales started slow — some days, nothing at all. Then, almost like magic, orders began trickling in. Friends and family jumped in to help pack and fulfill them. Everyone who tried the product loved it and left glowing reviews on our website. Most became repeat customers. We officially launched in November, and it took a couple of months to hit our stride. As of January 2026, we’re averaging $12,000 per month in sales.
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: