Large language models (LLMs) often fail with complex and medium difficulty tasks where structured templates do not apply. Bold claims about their capabilities, such as surpassing human pull requests, are misleading and based on flawed interpretations of data. The widespread adoption of LLMs in coding is evident, but their full capabilities are often overstated. Current LLM tools present limitations and frustrations, revealing the necessity for better understanding and expectations around their use while exploring custom solutions to enhance their effectiveness.
Many claims about large language models, including their ability to surpass human-generated pull requests, often lack rigor and overlook their limitations.
Generating a fully functional operating system using current LLM capabilities is highly improbable, revealing a misunderstanding of their technical limits.
Even though large language models can assist in coding, they come with substantial issues and should be used with a clear understanding of their capabilities.
The dashboard showing LLMs' PR performance primarily includes personal projects and auto-approvals, misrepresenting their effectiveness in meaningful contributions.
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