什么是AI Vibe编码？

AI Vibe编码是一种针对软件开发的新方法，可以根据精心制作的自然语言用户提示生成高级，可执行的代码。

The term "vibe coding" was popularized by OpenAI co-founder Andrej Karpathy, who described it as a state of mind or attitude adopted by programmers when working with AI-generated code. It can be contrasted with the traditional approach to software development, which involves humans writing code from scratch in a structured and modular fashion, with each segment of code being carefully reviewed and signed off on by peers or superiors in a process known as "code inspection."

“ Vibe编码”一词由Openai联合创始人Andrej Karpathy推广，他将其描述为使用AI生成的代码时采用程序员采用的心态或态度。它可以与传统的软件开发方法形成鲜明对比，该方法涉及人类以结构化和模块化的方式从头开始编写代码，每个代码的每个部分都会在称为“代码检查”的过程中仔细审查和签署。

In essence, vibe coding is about letting the AI take the lead and generate the bulk of the code, while the human programmer acts more as a guide or supervisor, providing input and feedback along the way. This is not to say that no manual labor is involved; rather, it is minimal and focused on high-level tasks such as selecting the appropriate framework or setting the desired style for the output.

从本质上讲，Vibe编码是关于让AI领先并生成大部分代码，而人类程序员则更多地充当指导或主管，并在此过程中提供意见和反馈。这并不是说没有涉及手动劳动。相反，它是最小的，并且专注于高级任务，例如选择适当的框架或为输出设置所需的样式。

For instance, if a programmer wants to build a web application using React, they might input a command such as "generate a React component for displaying a list of items" and leave the rest to the AI. The AI would then generate the component code, which the programmer would glance at, perhaps making a small adjustment or two before moving on to the next task.

例如，如果程序员想使用React构建Web应用程序，则他们可能会输入诸如“生成用于显示项目列表的React组件”之类的命令，然后将其余的留给AI。然后，AI将生成组件代码，程序员会瞥了一眼，可能会在继续执行下一个任务之前进行一两个小调整。

This approach to software development is made possible by the recent advances in artificial intelligence, specifically in the area of natural language processing (NLP) and deep learning. Researchers have trained AI models to understand and generate multiple programming languages, enabling them to translate human instructions into executable code.

人工智能的最新进展，特别是自然语言处理（NLP）和深度学习方面的最新进展使这种软件开发方法成为可能。研究人员已经培训了AI模型，以理解和生成多种编程语言，从而使他们能够将人类说明转化为可执行的代码。

One example of such a model is OpenAI's Codex, which is based on the same technology as the GPT-3 language model but trained on a massive dataset of code and text. Codex can perform various coding tasks, such as writing functions, translating code from one language to another, and even generating entire programs from scratch.

此类模型的一个示例是OpenAI的法典，该法典基于与GPT-3语言模型相同的技术，但在大量的代码和文本数据集上进行了培训。 Codex可以执行各种编码任务，例如编写功能，将代码从一种语言转换为另一种语言，甚至从头开始生成整个程序。

Another AI model capable of vibe coding is DeepMind's AlphaCode, which was specifically designed for competitive programming, a domain that usually requires advanced algorithms and data structures. According to the researchers, AlphaCode's output is comparable to the performance of the median human competitor in a recent programming competition.

DeepMind的字母是专门为竞争性编程设计的另一个AI模型，该模型通常需要高级算法和数据结构。根据研究人员的说法，AlphaCode的输出与最近的编程竞赛中中位人类竞争对手的表现相当。

While AI vibe coding can lead to faster software development results, there is a risk that the output generated from a vibe coding approach may not be adequately reviewed and signed off on, which is where traditional coding practices continue to offer safeguards.

尽管AI Vibe编码可以带来更快的软件开发结果，但可能不会对Vibe编码方法产生的输出产生的风险进行充分的审查和登录，这是传统的编码实践继续提供保障措施的地方。

Moreover, if the programmer's input is not clear or consistent, the AI may generate code that deviates from the intended goal, leading to bugs or unexpected behavior. In this sense, AI is a tool that amplifies the programmer's intent and capabilities, but it does not eliminate the need for human supervision and feedback.

此外，如果程序员的输入尚不清楚或不一致，则AI可能会生成偏离预期目标的代码，从而导致错误或意外行为。从这个意义上讲，AI是扩大程序员意图和功能的工具，但并不能消除对人类监督和反馈的需求。

As AI technology continues to improve, we may see even more interesting and useful applications of vibe coding in the future, blurring the lines between human creativity and machine intelligence.

随着AI技术的不断改进，我们可能会在将来看到氛围编码的更有趣和有用的应用，从而模糊了人类创造力和机器智能之间的界限。