Гангстер одним ударом расправился с туристом в Таиланде и попал на видео18:08
ChatGPT is a large language model that generates human-like
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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:
63-летняя Деми Мур вышла в свет с неожиданной стрижкой17:54
One thing that I found really interesting was the ability of the LLM to inspect the COM files for ZEXALL / ZEXCOM tests for the Z80, easily spot the CP/M syscalls that were used (a total of three), and implement them for the extended z80 test (executed by make fulltest). So, at this point, why not implement a full CP/M environment? Same process again, same good result in a matter of minutes. This time I interacted with it a bit more for the VT100 / ADM3 terminal escapes conversions, reported things not working in WordStar initially, and in a few minutes everything I tested was working well enough (but, there are fixes to do, like simulating a 2Mhz clock, right now it runs at full speed making CP/M games impossible to use).