The editor, Artem Kaptur, traded in markets related to YouTube and specifically, MrBeast. Kalshi says his transactions were initially flagged because of his "near-perfect trading success on markets with low odds, which were statistically anomalous." Because trades are public on Kalshi, multiple users also flagged the trades as suspicious. Kalshi learned Kaptur was an employee of MrBeast during its investigation and determined he "likely had access to material non-public information connected to his trading." Perhaps unsurprisingly, trading with insider information violates Kalshi's rules.
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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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