请问HN：关于将大型语言模型（LLMs）应用于编码的概念模型的资源有哪些？
我正在努力从概念上充分理解大型语言模型（LLMs），以便能够预测它们在生成代码方面的能力（和局限性）。这个目标合理吗？有没有好的资源推荐？
到目前为止，我查看了以下内容：
1. 《Vibe Coding》，作者：Steve Yegge 和 Gene Kim（https://www.amazon.in/Vibe-Coding-Building-Production-grade-Software/dp/1966280025）。这本书提供了一些实际示例和许多指导原则，但理论部分不多，似乎也没有从概念上解释大型语言模型。
2. 《Build an LLM from Scratch》，作者：Sebastian Raschka（https://www.manning.com/books/build-a-large-language-model-from-scratch）。这本书看起来很深入，但我并不想真正去“构建”一个大型语言模型。
3. 《AI Engineering》，作者：Chip Huyen（https://www.amazon.in/AI-Engineering-Building-Applications-Foundation/dp/1098166302）。这本书看起来很有前景，尽管它并不专注于编码。
也许类似于《How Claude Code Works》（https://code.claude.com/docs/en/how-claude-code-works），但内容可以更详细一些。
谢谢。
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I am trying to understand LLMs conceptually well enough to be able to predict their capabilities (and limitations) when it comes to generating code. Is that even a sensible goal? Are there good resources?<p>So far I've looked at:<p>1. Vibe Coding, by Steve Yegge and Gene Kim (https://www.amazon.in/Vibe-Coding-Building-Production-grade-Software/dp/1966280025). This has some practical examples and many guidelines. But there is not much theory and this does not explain LLMs conceptually AFAICT.<p>2. Build an LLM from Scratch, by Sebastian Raschka (https://www.manning.com/books/build-a-large-language-model-from-scratch). Seems in-depth. But I don't really want to <i>build</i> an LLM.<p>3. AI Engineering, by Chip Huyen (https://www.amazon.in/AI-Engineering-Building-Applications-Foundation/dp/1098166302). This seems promising, although it is not coding focussed.<p>Perhaps something like How Claude Code Works (https://code.claude.com/docs/en/how-claude-code-works) but fleshed out in more detail.<p>Thanks.