资讯 | 大神Andrej Karpathy提出Vibe 编码：提示、明白、后悔？尚未发现的 Vibe 趋势的风险

“Just dictate your digital dreams… the AI’s listening (mostly).”

“只需口述您的数字梦想......AI 在倾听（大部分）。

For those who haven’t caught the bug yet, vibe coding is a term coined by ex-OpenAI and Tesla AI wizard Andrei Karpathy.

对于那些还没有抓住错误的人来说，氛围编码是前 OpenAI 和特斯拉 AI 奇才 Andrei Karpathy 创造的一个术语。

The idea is to let AI do the heavy lifting while you focus on the bigger picture. Supporters argue vibe-coding free developers from the constraints of manual coding, making development faster and simpler.

这个想法是让 AI 完成繁重的工作，而您则专注于更大的图景。支持者认为，氛围编码使开发人员摆脱了手动编码的束缚，使开发更快、更简单。

The promise? Utterly seductive. Imagine whispering your wildest app dreams into the digital ear of an AI genie, and poof — functional code appears! No more wrestling with semicolons at 3 AM, no more arcane syntax rules standing between your brilliant idea and reality. Just pure, unadulterated vibes translating into software. Sounds like a dream, right? Like ordering a custom-built spaceship with a Post-it note.

承诺？完全诱人。想象一下，将您最疯狂的应用程序梦想悄悄地对着 AI 精灵的数字耳朵说出， 然后噗的一声 - 功能代码出现了！不再需要在凌晨 3 点与分号搏斗，不再有晦涩难懂的语法规则阻碍您的绝妙想法和现实。只是纯粹、 纯粹的氛围转化为软件。听起来像个梦，对吧？就像订购一艘带有便利贴的定制宇宙飞船一样。

The promise? Utterly seductive. Imagine whispering your wildest app dreams into the digital ear of an AI genie, and poof — functional code appears! No more wrestling with semicolons at 3 AM, no more arcane syntax rules standing between your brilliant idea and reality. Just pure, unadulterated vibes translating into software. Sounds like a dream, right? Like ordering a custom-built spaceship with a Post-it note.

承诺？完全诱人。想象一下，将您最疯狂的应用程序梦想悄悄地对着 AI 精灵的数字耳朵说出， 然后噗的一声 - 功能代码出现了！不再需要在凌晨 3 点与分号搏斗，不再有晦涩难懂的语法规则阻碍您的绝妙想法和现实。只是纯粹、 纯粹的氛围转化为软件。听起来像个梦，对吧？就像订购一艘带有便利贴的定制宇宙飞船一样。

This concept exploded into the mainstream consciousness thanks, in part, to AI visionary Andrej Karpathy’s musings back in February 2025, describing it as a state where you “fully give in to the vibes, embrace exponentials, and forget that the code even exists” (as cited in Vibe coding — Wikipedia, n.d.). The idea is intoxicating: focus on the what, let the AI handle the how. From entrepreneurs sketching MVPs on napkins to seasoned developers looking to shortcut the grunt work, the allure is undeniable.

这个概念在一定程度上要归功于 AI 远见者 Andrej Karpathy 在 2025 年 2 月的沉思，将其描述为一种状态，你“完全屈服于共鸣，拥抱指数，甚至忘记代码的存在”（如 Vibe 编码 — 维基百科，日期不详）。这个想法令人陶醉：专注于什么 ，让 AI 处理如何 。从在餐巾纸上画出 MVP 的企业家到希望走捷径完成繁重工作的经验丰富的开发人员，其吸引力是不可否认的。

Andrej Karpathy’s original tweet on vibe-coding

Andrej Karpathy 关于 vibe 编码的原始推文

The idea has received a lot of attention, with every AI influencer jumping on the vibe-coding bandwagon and even industry leaders like Anthropic’s CEO Dario Amodei, predicting that within 12 months, virtually all code will be written by AI.

这个想法受到了很多关注，每个 AI 影响者都加入了氛围编码的潮流，甚至像 Anthropic 的首席执行官 Dario Amodei 这样的行业领导者也预测，在 12 个月内，几乎所有代码都将由 AI 编写。

Confronting the full spectrum of societal consequences and responsible innovation challenges hidden beneath the surface of the jubilant vibe coding trend.

面对隐藏在欢快氛围编码趋势表面之下的全方位社会后果和负责任的创新挑战。

The Core Idea: What Is Vibe Coding?

核心思想：什么是 Vibe 编码？

At its core, vibe coding uses Large Language Models (LLMs) like ChatGPT, Claude, or GitHub Copilot to translate natural language descriptions into functional code. The focus is no longer on line-by-line implementation but expressing intent and guiding the AI to create the solution.

Vibe 编码的核心是使用 ChatGPT、Claude 或 GitHub Copilot 等大型语言模型 （）LLMs 将自然语言描述转换为功能代码。重点不再是逐行实施，而是表达意图并指导 AI 创建解决方案。

So, what’s the secret sauce behind vibe coding? At its heart, it’s an AI-fueled programming paradigm where you, the human, describe what you want your software to do in plain English (or your natural language of choice). You feed this description — your “vibe” — as a prompt to a Large Language Model (LLM) specially trained on mountains of code (Vibe coding — Wikipedia, n.d.; Replit, n.d.-b). Think of it like commissioning a painting by describing the feeling you want it to evoke, rather than specifying every brushstroke.

那么，vibe 编码背后的秘密武器是什么？从本质上讲，它是一个 AI 驱动的编程范式，你（人类）用简单的英语（或你选择的自然语言）描述你希望你的软件做什么。你把这个描述——你的“vibe”——作为提示提供给一个大型语言模型 （LLM），该模型经过大量代码的专门训练（Vibe 编码 — 维基百科，n.d.;雷普利特，n.d.-b）。把它想象成委托一幅画，通过描述你希望它唤起的感觉 ，而不是指定每一个笔触。

The LLM then does the heavy lifting, churning out the source code needed to (hopefully) bring your vision to life. Your role shifts from meticulous coder to something more akin to an orchestral conductor, guiding the AI, testing its output, and refining the results (Vibe coding — Wikipedia, n.d.). It’s about communicating intent, focusing on the application’s goals, its look and feel, and the user experience, while the AI sweats the syntactical details (Replit, n.d.-b).

然后，他们LLM完成了繁重的工作，制作出（希望）将您的愿景变为现实所需的源代码。你的角色从一丝不苟的编码员转变为更类似于管弦乐队指挥的角色，指导 AI，测试其输出，并完善结果（Vibe 编码 — 维基百科，日期不详）。它是关于传达意图，专注于应用程序的目标、外观和用户体验，而 AI 则关注语法细节 （Replit， n.d.-b）。

Pro Tip: While vibe coding is great for brainstorming or simple tasks, complex logic often requires more specific instructions than just a “vibe.” Think of it as ordering food: “Make me something tasty” might get you a meal, but “Make me a medium-rare steak with a side of asparagus” is far more likely to deliver what you actually envisioned.

专业提示： 虽然 vibe 编码非常适合头脑风暴或简单的任务，但复杂的逻辑通常需要更具体的指令，而不仅仅是“vibe”。把它想象成点餐：“给我做点好吃的”可能会让你吃一顿饭，但“给我做一块半熟牛排配芦笋”更有可能实现你实际设想的东西。

The appeal is undeniable:

呼吁是不可否认的：

• Speed: Generate boilerplate, components, or even entire scripts in seconds.

• 速度 ：在几秒钟内生成样板、组件甚至整个脚本。

• Exploration: Quickly prototype ideas and dive into new technical areas with ease.

• 探索 ：快速构建想法原型并轻松深入研究新的技术领域。

• Accessibility: Make coding more approachable for those unfamiliar with specific syntax or libraries.

• 辅助功能 ：使编码对于不熟悉特定语法或库的用户更易于理解。

  
  

Yet, behind this effortless facade there are serious challenges to deal with.

然而，在这个轻松的外表背后，有严峻的挑战需要应对。

Coding vs. Programming: A Key Distinction，Not All AI Assistance is Vibin’

编码与编程：一个关键区别，并非所有的 AI 辅助都是 Vibin'

The term truly caught fire after Andrej Karpathy’s evocative description painted a picture of effortless, exponential creation (as cited in Vibe coding — Wikipedia, n.d.). It tapped into a collective desire to transcend the often-tedious implementation details and leap straight to innovation (Replit, n.d.-b).

在 Andrej Karpathy 令人回味的描述描绘了一幅毫不费力的指数级创造图景之后，这个词真正引起了轰动（如 Vibe 编码 — 维基百科，日期不详）。它利用了一种集体愿望，即超越通常乏味的实施细节，直接跃入创新（Replit，n.d.-b）。

However, it’s crucial to draw a line, as AI researcher Simon Willison points out. If you’re using an LLM to generate code, but you meticulously review, test, and understand every single line before integrating it, that’s not quite vibe coding. That’s more like having a super-powered autocomplete or a tireless coding assistant (Willison, 2025; Vibe coding — Wikipedia, n.d.). True vibe coding often involves a leap of faith — accepting the AI’s output without necessarily grasping all the nuts and bolts under the hood (Vibe coding — Wikipedia, n.d.). It’s this “trust me, bro” relationship with the AI that distinguishes vibe coding and, as we’ll see, is where some of the stickiest issues lie. The magic happens thanks to sophisticated LLMs trained on colossal datasets of code and text, enabling them to translate our fuzzy human intentions into concrete machine instructions (Replit, n.d.-b).

然而，正如 AI 研究员 Simon Willison 所指出的那样，划清界限至关重要。如果你正在使用 an LLM 来生成代码，但在集成之前仔细审查、测试和理解每一行代码，那并不完全是 vibe 编码。这更像是一个超能力的自动完成或一个不知疲倦的编码助手（Willison，2025 年;Vibe 编码 — 维基百科，日期不详）。真正的 vibe 编码通常涉及信仰的飞跃 — 接受 AI 的输出，而不必抓住引擎盖下的所有细节（Vibe 编码 — 维基百科，日期不详）。正是这种与 AI 的“相信我，兄弟”的关系使 vibe 编码与众不同，正如我们将看到的，这也是一些最棘手的问题所在。奇迹的发生要归功于在巨大的代码和文本数据集上进行复杂的LLMs训练，使他们能够将我们模糊的人类意图转化为具体的机器指令 （Replit， n.d.-b）。

“Review & Refine vs. Trust & Go: Two flavors of AI-assisted coding.”

“审查与改进与信任与开始：人工智能辅助编码的两种风格。”

Trivia: The datasets used to train code-generating LLMs are massive, often containing billions of lines of code scraped from public repositories like GitHub. This includes code in countless languages, covering everything from simple scripts to complex operating systems.

琐事： 用于训练代码生成的LLMs数据集非常庞大 ，通常包含从 GitHub 等公共存储库中抓取的数十亿行代码。这包括无数种语言的代码，涵盖从简单脚本到复杂作系统的所有内容。

To understand the limits of vibe coding, we must first recognize that coding and programming are two different skills:

要了解 vibe 编码的局限性，我们必须首先认识到编码和编程是两种不同的技能：

• Coding is the act of writing code — translating logic and requirements into instructions a computer can understand.

• 编码是编写代码的行为 — 将逻辑和需求转换为计算机可以理解的指令。

• Programming is much broader discipline, an art and science that requires creativity, problem-solving, and intuition honed through experience.

• 编程是一门更广泛的学科，是一门艺术和科学，需要创造力、解决问题的能力和通过经验磨练的直觉。

  
  

LLMs excel at coding tasks based on the patterns they’ve learned from GitHub and Stack Overflow. They can replicate common solutions, but they lack true understanding and the contextual awereness required for real-world programming.

LLMs擅长根据他们从 GitHub 和 Stack Overflow 学到的模式对任务进行编码。他们可以复制常见的解决方案，但他们缺乏真正的理解和实际编程所需的上下文洞察力。

Programming include things like:

编程包括以下内容：

• Understanding requirements

• 了解要求

• Designing system architecture

• 设计系统架构

• Ensuring scalability and performance

• 确保可扩展性和性能

• Debugging and troubleshooting issues

• 调试和排查问题

• Considering security risk and implications

• 考虑安全风险和影响

• Applying creativity and intuition to solve novel problems

• 运用创造力和直觉来解决新问题

  
  

This is where human oversight remains irreplaceable. If you’re just throwing AI-generated code into production without understanding what’s happening under the hood, you’re asking for trouble.

这就是人类监督仍然是不可替代的地方。如果您只是将 AI 生成的代码投入生产，而不了解幕后发生的事情，那么您就是在自找麻烦。

Where the Vibes Flow: Applications and Use Cases

共鸣的流动方向：应用和用例

From Zero to MVP: The Need for Speed

从零到 MVP：极品飞车

One of the most celebrated applications of vibe coding is its potential to turbocharge the early stages of software development. Got a brilliant idea but facing a mountain of backlog? Vibe coding promises to turn those “someday” concepts into tangible Minimum Viable Products (MVPs) in weeks, not years (Security Journey, 2025; Replit, n.d.-b). This rapid prototyping capability allows for quicker iteration, faster validation of ideas, and potentially slashes the time-to-market (Security Journey, 2025). Think of it as hitting the fast-forward button on innovation.

vibe 编码最著名的应用之一是它有可能为软件开发的早期阶段提供动力。有一个绝妙的想法，但面临堆积如山的积压工作？Vibe 编码承诺在几周而不是几年内将这些“有一天”的概念转化为有形的最小可行产品 （MVP）（Security Journey，2025 年;雷普利特，n.d.-b）。这种快速原型设计功能允许更快的迭代、更快的想法验证，并可能缩短上市时间（Security Journey，2025 年）。把它想象成按下创新的快进按钮。

“Idea to App at Warp Speed: Vibe coding the MVP.”

“以 warp 速度从创意到应用程序：Vibe 为 MVP 编码。”

Pro Tip: Use vibe coding for MVPs to test core concepts quickly, but plan for significant refactoring or rewriting if the prototype proves successful and needs to scale into a production-ready application. The initial “vibed” code might not have the robustness or structure required for the long haul.

专业提示： 对 MVP 使用 vibe 编码来快速测试核心概念，但如果原型证明成功并且需要扩展到生产就绪的应用程序，请计划进行重大重构或重写。最初的 “vibed” 代码可能不具备长期所需的健壮性或结构。

Code Without Credentials: Empowering the Non-Coder

无凭证代码：赋予非编码人员权力

Perhaps the most revolutionary promise of vibe coding is its potential to democratize software creation. Entrepreneurs, designers, educators, scientists — anyone with a specific need or a novel idea, but lacking traditional coding skills — can potentially become creators (Replit, n.d.-b). Vibe coding aims to dismantle the technical barriers, offering a simplified path from concept to functional application (Replit, n.d.-b). This could usher in an era of hyper-personalized software, “software for one,” where individuals craft bespoke tools tailored perfectly to their unique workflows or problems (Vibe coding — Wikipedia, n.d.). No need to learn Python or JavaScript first; just articulate your vision.

也许 vibe 编码最具革命性的承诺是它有可能使软件创建大众化。企业家、设计师、教育家、科学家——任何有特定需求或新奇想法但缺乏传统编码技能的人——都有可能成为创造者（Replit， n.d.-b）。Vibe 编码旨在消除技术障碍，提供从概念到功能应用程序的简化路径 （Replit， n.d.-b）。这可能会迎来一个超个性化软件的时代，即“软件为一个人”，在这个时代，个人会根据他们独特的工作流程或问题制作完美的定制工具（Vibe 编码 — 维基百科，日期不详）。无需先学习 Python 或 JavaScript;只需阐明你的愿景。

“You don’t need a CS degree to build your dream app anymore. Just good vibes (and a good prompt).”

“你不再需要 CS 学位来构建你梦想中的应用程序。只是良好的氛围（和良好的提示）。

Trivia: The idea of generating code from natural language isn’t entirely new. Early attempts date back decades, but it’s the recent breakthroughs in LLM scale and capability that have made concepts like vibe coding practical.

琐事： 从自然语言生成代码的想法并不是全新的。早期的尝试可以追溯到几十年前，但最近在规模和功能方面的LLM突破使 vibe 编码等概念变得实用。

Banishing Boilerplate: A Helping Hand for Pros

消除样板：专业人士的帮手

Even seasoned developers stand to benefit. Let’s face it, a significant chunk of coding involves writing repetitive boilerplate, setting up standard frameworks, or implementing mundane functionalities. Vibe coding offers the possibility of outsourcing this drudgery to AI agents (Security Journey, 2025; Replit, n.d.-b). By letting the AI handle the “grunt work,” developers can reclaim precious time and cognitive energy to focus on the challenging, creative, and truly impactful parts of software engineering — the complex algorithms, the novel architectures, the elegant solutions (Replit, n.d.-b). Less tedious typing, more high-level thinking. Sounds like a productivity win-win.

即使是经验丰富的开发人员也会受益。让我们面对现实吧，很大一部分编码涉及编写重复的样板、设置标准框架或实现普通功能。Vibe 编码提供了将这种苦差事外包给 AI 代理的可能性（Security Journey，2025 年;雷普利特，n.d.-b）。通过让 AI 处理“繁重的工作”，开发人员可以回收宝贵的时间和认知能量，专注于软件工程中具有挑战性、创造性和真正有影响力的部分——复杂的算法、新颖的架构、优雅的解决方案（Replit，n.d.-b）。更少的繁琐打字，更多的高级思考。听起来像是生产力双赢。

“Let the AI handle the snooze-fest code. You’ve got bigger fish to fry (or bugs to squash).”

“让 AI 处理 snooze-fest 代码。你有更大的鱼要煎（或虫子要压扁）。

Pro Tip for Developers: Use AI code generation tools strategically. They excel at well-defined, common tasks. Integrate them into your workflow for speed, but always maintain oversight and apply your expertise to the critical, unique aspects of your project. Don’t let the tool dictate the architecture.

开发人员的专业提示： 有策略地使用 AI 代码生成工具。他们擅长定义明确的常见任务。将它们集成到您的工作流程中以加快速度，但始终保持监督并将您的专业知识应用于项目的关键、独特方面。不要让工具决定架构。

Learning the Ropes (with AI Training Wheels)

学习绳索（使用 AI 训练轮）

For those dipping their toes into the vast ocean of programming, vibe coding can seem like a friendly life raft. Instead of facing the steep initial learning curve of syntax and logic from scratch, beginners can start with AI-generated code that works (or mostly works) (Replit, n.d.-b). By examining this code and making small tweaks, they can gain a more intuitive, hands-on understanding of programming concepts. Seeing the immediate impact of changes and using AI to help debug can make the learning process less intimidating and potentially more engaging (Replit, n.d.-b; Security Journey, 2025). It’s like learning to ride a bike with very sophisticated training wheels.

对于那些涉足浩瀚编程海洋的人来说，vibe 编码似乎是一个友好的救生筏。初学者可以从 AI 生成的有效 （或大部分有效）代码开始，而不是从头开始面对语法和逻辑的陡峭初始学习曲线（Replit，n.d.-b）。通过检查此代码并进行小的调整，他们可以更直观地了解编程概念。看到变化的直接影响并使用 AI 来帮助调试可以使学习过程不那么令人生畏，并且可能更具吸引力（Replit， n.d.-b;安全之旅，2025 年）。这就像学习骑自行车时，会有非常复杂的辅助轮。

“Coding 101, Vibe Edition: Getting a feel for the code without the initial crash course.”

“编码 101，Vibe 版：无需初始速成课程即可感受代码。”

Trivia: Some educational platforms are already integrating AI coding assistants to provide personalized feedback and explanations to students, potentially accelerating the learning process for basic programming skills.

琐事： 一些教育平台已经集成了 AI 编码助手，为学生提供个性化的反馈和解释，从而可能加快基本编程技能的学习过程。

Hobby Projects and Quick Hacks: Just for Fun

业余爱好项目和快速技巧：只是为了好玩

Finally, let’s not forget the sheer fun factor. Vibe coding is often touted as perfect for whipping up simple apps, weekend passion projects, or those quirky little tools you wish existed just for you (Reddit user comment, as cited in Reddit, n.d.-a; Gitpod, 2025). If you’re not aiming to build the next enterprise-grade system, but just want to create something cool quickly without getting bogged down in coding theory, vibe coding offers an appealingly direct route from idea to execution (Replit, n.d.-b). It makes experimentation fast and cheap, encouraging playful exploration of software possibilities (Gitpod, 2025).

最后，我们不要忘记纯粹的乐趣因素。Vibe 编码经常被吹捧为非常适合制作简单的应用程序、周末激情项目或您希望只为您存在的那些古怪的小工具（Reddit 用户评论，如 Reddit 中引用的，n.d.-a;Gitpod，2025 年）。如果你的目标不是构建下一个企业级系统，而只是想快速创建一些很酷的东西，而不会陷入编码理论的泥潭，那么 vibe 编码提供了一条从想法到执行的有吸引力的直接路线（Replit，n.d.-b）。它使实验变得快速且便宜，鼓励对软件可能性的有趣探索（Gitpod，2025 年）。

“Building that ‘Wouldn’t it be cool if…’ app in an afternoon? That’s the vibe.”

“建立那个'如果......岂不是很酷吗？'应用程序？这就是氛围。

Pro Tip: Hobby projects are a fantastic sandbox for experimenting with vibe coding. The stakes are lower, allowing you to explore the capabilities and limitations of AI code generation without risking critical production systems.

专业提示：Hobby projects 是用于试验 vibe 编码的绝佳沙盒。风险更低，使您能够探索 AI 代码生成的功能和限制，而不会冒关键生产系统的风险。

The Hype Train vs. The Reality Check: Cheers and Jeers

炒作列车与现实检查：欢呼和嘲笑

All Aboard! The Case for Vibe Coding

全体上船！Vibe 编码案例

The enthusiasm for vibe coding isn’t just hot air; proponents point to tangible benefits that could reshape software development.

对 vibe 编码的热情不仅仅是空谈;支持者指出了可以重塑软件开发的切实好处。

• Productivity Rocket Fuel: Imagine generating code orders of magnitude faster than human typing speed (Gitpod, 2025). Advocates see AI assistants churning out working features while developers focus on the next big idea, leading to potentially exponential gains in efficiency and dramatically shorter development cycles (Gitpod, 2025; Security Journey, 2025). Less time coding, more time shipping.

• 生产力火箭燃料： 想象一下，生成代码的速度比人类打字速度快几个数量级（Gitpod，2025 年）。倡导者看到 AI 助手大量推出工作功能，而开发人员则专注于下一个大创意，从而可能带来指数级的效率提升和显著缩短的开发周期（Gitpod，2025 年;安全之旅，2025 年）。更少的编码时间，更多的运输时间。

• Creativity Unleashed: By automating the mundane, error-prone parts of coding, vibe coding promises to free up developers’ mental bandwidth for the truly creative work: envisioning new possibilities, exploring innovative solutions, and focusing on the art of software design (Gitpod, 2025). It shifts the focus from syntax wrangler to digital architect.

• 创造力释放： 通过自动化编码中平凡、容易出错的部分，vibe 编码有望为开发人员释放脑力带宽，让他们从事真正的创造性工作：设想新的可能性，探索创新解决方案，并专注于软件设计的艺术 （Gitpod，2025 年）。它将重点从语法管理者转移到数字架构师。

• Coding for the People: This is perhaps the most powerful argument — breaking down the walls around software creation. Vibe coding could empower a whole new generation of creators — entrepreneurs, artists, scientists, educators — turning anyone with a vision into a potential builder (Replit, n.d.-b). This democratization is especially appealing in fast-moving domains like Web3, where rapid iteration is key (Bitget News, 2025).

• 为人民编码： 这也许是最有力的论点 — 打破围绕软件创建的壁垒。Vibe 编码可以赋予全新一代创作者（企业家、艺术家、科学家、教育工作者）的能力，将任何有远见的人变成潜在的建设者（Replit，n.d.-b）。这种民主化在 Web3 等快速发展的领域尤其有吸引力，在这些领域中，快速迭代是关键（Bitget News，2025 年）。

• Learning Curve, Smoothed: As mentioned, it offers a potentially less intimidating on-ramp for programming newcomers, fostering practical understanding through interaction and modification rather than abstract theory alone (Replit, n.d.-b; Security Journey, 2025). Faster feedback loops, lower stress.

• 学习曲线，平滑： 如前所述，它为编程新手提供了一个可能不那么令人生畏的入口，通过交互和修改而不是仅仅抽象理论来促进实践理解（Replit， n.d.-b;安全之旅，2025 年）。更快的反馈循环，更低的压力。

• Fun & Exploration: Let’s not discount the joy of rapid creation! Vibe coding makes it easier to quickly prototype wild ideas or build personal tools just for the fun of it, fostering a spirit of experimentation (Gitpod, 2025).

• 乐趣与探索： 我们不要低估快速创作的乐趣！Vibe 编码可以更轻松地快速构建疯狂想法的原型或构建个人工具，从而培养实验精神（Gitpod，2025 年）。

  
  

“More speed, more creators, more fun? The sunny side of the vibe coding street.”

“更快的速度、更多的创作者、更多的乐趣？氛围编码街的阳光面。

Pro Tip: To maximize the benefits, treat AI code generators as collaborators. Provide clear context, refine the prompts based on output, and use your domain knowledge to guide the AI toward the desired outcome. Don’t just prompt and pray.

专业提示： 为了最大限度地发挥优势，请将 AI 代码生成器视为协作者。提供清晰的上下文，根据输出优化提示，并利用您的领域知识指导 AI 实现预期的结果。不要只是提示和祈祷。

Hold On, Concerns and Criticisms Emerge

等一下，担忧和批评出现

Now, let’s pump the brakes and listen to the chorus of concerns. The path of vibe coding isn’t paved entirely with gold; there are some significant potholes and potential cliff edges.

现在，让我们踩刹车，听听大家的担忧。氛围编码的道路并不完全是用金子铺成的;有一些明显的坑洼和潜在的悬崖边缘。

• The Black Box Problem: Understanding & Maintainability: This is a big one. If you’re using code you don’t fully understand, how can you effectively debug it when things go wrong? How can you maintain or evolve it over time? Relying on AI-generated code without comprehension can lead to fragile, unmaintainable systems riddled with hidden flaws (Security Journey, 2025; Vibe coding — Wikipedia, n.d.). Building a production system via pure vibe coding is widely seen as playing with fire (Vibe coding — Wikipedia, n.d.).

• 黑匣子问题：理解与可维护性： 这是一个很大的问题。如果你使用的代码不完全理解，那么当出现问题时，如何有效地调试它呢？您如何随着时间的推移维护或发展它？在不理解的情况下依赖 AI 生成的代码可能会导致系统脆弱、无法维护，并充满隐藏的缺陷（Security Journey，2025 年;Vibe 编码 — 维基百科，日期不详）。通过纯 vibe 编码构建生产系统被广泛视为玩火（Vibe 编码 — 维基百科，日期不详）。

• Security? What Security?: Experts are sounding the alarm bells loud and clear. AI models learn from vast datasets, including code that might be insecure or outdated. Generating code without understanding its security implications can introduce critical vulnerabilities (Security Journey, 2025; Legit Security, 2025). It creates a scenario where developers “don’t know what they don’t know,” potentially shipping ticking time bombs, especially when handling sensitive data (Security Journey, 2025).

• 安全？What Security？： 专家们正在响亮而清晰地敲响警钟。AI 模型从庞大的数据集中学习，包括可能不安全或过时的代码。在不了解其安全影响的情况下生成代码可能会引入关键漏洞（Security Journey，2025 年;Legit Security，2025 年）。它创造了一种情况，开发人员“不知道他们不知道什么”，可能会运送定时炸弹，尤其是在处理敏感数据时（Security Journey，2025 年）。

• Hallucinating Code & Stubborn Bugs: AI models aren’t infallible. They can, and do, make mistakes. They might generate code with subtle (or glaring) logical errors, misunderstand requirements, produce inefficient solutions, or even “hallucinate” features or libraries that don’t exist (Security Journey, 2025; Cendyne.dev, 2025). Remember, they’re trained on existing code, warts and all, including potentially sloppy or incorrect examples (Cendyne.dev, 2025).

• 幻觉代码和顽固的错误：AI 模型并非万无一失。他们可能会犯错误，而且确实会犯错误。它们可能会生成具有细微（或明显）逻辑错误的代码，误解需求，产生低效的解决方案，甚至“幻觉”不存在的功能或库（Security Journey，2025 年;Cendyne.dev，2025 年）。请记住，他们接受了现有代码、疣子等的训练，包括可能草率或不正确的示例（Cendyne.dev，2025 年）。

• Skill Atrophy & The Illusion of Competence: Is convenience making us dumber? There’s a real concern that over-reliance on AI code generation could lead to an erosion of fundamental programming skills, critical thinking, and problem-solving abilities, especially among learners (Security Journey, 2025; Adnovum, 2025). You might feel like you’re coding, but are you truly learning if the AI does all the heavy lifting (Reddit user comment, as cited in Reddit, n.d.-b)?

• 技能萎缩与能力的幻觉： 便利让我们变得更愚蠢吗？人们真正担心的是，过度依赖 AI 代码生成可能会导致基本编程技能、批判性思维和解决问题的能力受到侵蚀，尤其是在学习者中（Security Journey，2025 年;Adnovum，2025 年）。你可能会觉得自己在编码，但你真的在学习 AI 是否完成了所有繁重的工作（Reddit 用户评论，如 Reddit 中引用的，n.d.-b）？

• Quality Limits & Context Blindness: While AI might nail simple tasks, its ability to handle complex, nuanced software requiring deep architectural understanding is still questionable (Cendyne.dev, 2025; DEV Community post, as cited in McNulty, 2025). Current LLMs often have limited context windows, meaning they might not see the bigger picture, suggest reusing existing code effectively, or maintain design consistency across a large project (Cendyne.dev, 2025).

• 质量限制和上下文盲性： 虽然 AI 可能会完成简单的任务，但它处理需要深入架构理解的复杂、细微的软件的能力仍然值得怀疑（Cendyne.dev，2025 年;DEV 社区帖子，引自 McNulty，2025 年）。Current LLMs 的上下文窗口通常有限，这意味着他们可能无法看到更大的图景，建议有效地重用现有代码，或在整个大型项目中保持设计一致性（Cendyne.dev，2025 年）。

• The Thorny Ethics Patch: Ownership, Liability, Bias: Who owns AI-generated code? Who is liable if it fails catastrophically or causes harm? How do we ensure the AI isn’t perpetuating biases hidden in its training data? These complex ethical and legal questions are lagging behind the technology’s rapid advance (Adnovum, 2025; Pearlmutter et al., 2024).

• 荆棘道德补丁：所有权、责任、偏见： 谁拥有 AI 生成的代码？如果它发生灾难性故障或造成伤害，谁来负责？我们如何确保 AI 不会延续其训练数据中隐藏的偏见？这些复杂的道德和法律问题落后于技术的快速发展（Adnovum，2025 年;Pearlmutter et al.， 2024）。

• “Will AI Take My Job?”: And of course, the million-dollar question (or perhaps, the zero-dollar salary question). While some see AI as an augmentation tool, others fear significant disruption in the software engineering job market, potentially squeezing out junior developers as AI tackles more foundational tasks (Adnovum, 2025; Reddit user comment, as cited in Reddit, n.d.-b).

• “AI 会抢走我的工作吗？” 当然，还有百万美元的问题（或者也许是零美元工资的问题）。虽然有些人将 AI 视为一种增强工具，但另一些人担心软件工程就业市场会受到重大干扰，随着 AI 处理更多基础任务，可能会排挤初级开发人员（Adnovum，2025 年;Reddit 用户评论，引自 Reddit，n.d.-b）。

  
  

“When the ‘vibes’ lead you down a dark alley of bugs and security holes.”

“当'共鸣'带你走进一条充满漏洞和安全漏洞的黑暗小巷时。”

Pro Tip: Never blindly trust AI-generated code in production environments. Rigorous testing, security scanning, and human code review are non-negotiable, especially for critical systems or code handling sensitive information. Assume the AI made mistakes until proven otherwise.

专业提示： 永远不要盲目相信生产环境中 AI 生成的代码。严格的测试、安全扫描和人工代码审查是没有商量余地的，尤其是对于关键系统或处理敏感信息的代码。假设 AI 犯了错误，直到证明不是这样。

When Vibe Coding Goes Wrong

当 Vibe Coding 出错时

There are already stories popping up online, like the indie hacker who vibe-coded a SaaS product. He even landed real paying customers — a huge win for any indie developer.

网上已经出现了一些故事，比如对 SaaS 产品进行 vibe 编码的独立黑客。他甚至获得了真正的付费客户——对于任何独立开发者来说都是一个巨大的胜利。

But upon learning of his success, the internet trolls quickly tore it to pieces:

但在得知他的成功后，互联网喷子很快就把它撕成碎片：

In the end, his app was taken down, and he had to beg for his job back at Popeyes.

最后，他的应用程序被下架了，他不得不乞求回到 Popeyes 的工作。

This is the danger of unchecked vibe coding — if you don’t understand what you’re doing, things will fall apart quickly. But that doesn’t mean vibe coding is useless — it just needs the right guardrails.

这就是未经检查的 vibe 编码的危险 — 如果您不理解自己在做什么，事情很快就会分崩离析。但这并不意味着 vibe 编码毫无用处——它只需要正确的护栏。

So how can you make vibe-coding work without setting yourself up for disaster?

那么，如何使 vibe 编码正常工作而不会让自己陷入灾难呢？

The Responsible AI Tightrope: Balancing Innovation and Impact

负责任的 AI 走钢丝：平衡创新和影响

Vibe coding isn’t just a technical trend; it’s a phenomenon with profound implications for Responsible AI principles. We need to walk a fine line, embracing innovation while diligently managing the risks.

Vibe 编码不仅仅是一种技术趋势;这种现象对负责任的 AI 原则具有深远的影响。我们需要谨慎行事，在努力管理风险的同时拥抱创新。

Security Nightmares: Code That Bites Back

安全噩梦：令人反感的代码

Let’s be blunt: AI code generators are known to produce insecure code. Research consistently flags this issue, with studies suggesting a startlingly high percentage (sometimes nearly half!) of AI-generated code snippets contain exploitable vulnerabilities (CSET, 2025a; CSET, 2025b). These aren’t just minor oopsies; we’re talking classic security blunders like SQL injection, cross-site scripting (XSS), insecure handling of sensitive data, and pulling in unsafe dependencies (ITPro, 2025; SecureFlag, 2024).

坦率地说：众所周知，AI 代码生成器会产生不安全的代码。研究一致地指出了这个问题，研究表明，AI 生成的代码片段中高得惊人的比例（有时接近一半）包含可利用的漏洞（CSET，2025a;CSET，2025b）。这些不仅仅是小问题;我们谈论的是经典的安全错误，如 SQL 注入、跨站点脚本 （XSS）、不安全地处理敏感数据以及引入不安全的依赖项（ITPro，2025 年;SecureFlag，2024 年）。

Why? Because the AI learns from the vast ocean of public code, which, frankly, contains a lot of insecure practices (All Things Open, 2025). An inexperienced user “vibing” their way to an application might unknowingly deploy code with gaping security holes, creating easy targets for attackers (Pearlmutter et al., 2024). It’s like building a house with instructions copied from random blueprints found online — some might be solid, others dangerously flawed.

为什么？因为 AI 从浩瀚的公共代码海洋中学习，坦率地说，其中包含许多不安全的做法（All Things Open，2025 年）。没有经验的用户可能会在不知不觉中部署具有巨大安全漏洞的代码，从而很容易成为攻击者的目标（Pearlmutter 等人，2024 年）。这就像用从网上找到的随机蓝图中复制的说明建造一座房子——有些可能是坚固的，有些可能是危险的缺陷。

“Your AI coding assistant might be building bridges… or leaving the castle gates wide open.”

“您的 AI 编码助手可能正在搭建桥梁......或者让城堡的大门敞开。

Pro Tip: Integrate security scanning tools (SAST, DAST, SCA) early and often in workflows involving AI-generated code. Treat AI code suggestions with healthy skepticism, especially regarding input validation, authentication, authorization, and data handling.

专业提示： 尽早并经常在涉及 AI 生成代码的工作流中集成安全扫描工具（SAST、DAST、SCA）。以合理的怀疑态度对待 AI 代码建议，尤其是在输入验证、身份验证、授权和数据处理方面。

Prompt Injection: When AI Listens to the Wrong Vibes

提示注入：当 AI 监听错误的共鸣时

Vibe coding relies on natural language prompts. Unfortunately, this opens the door to prompt injection attacks (IBM, n.d.). Imagine a malicious actor crafting a prompt that looks innocent but secretly instructs the LLM to do something harmful — like leak sensitive data, bypass security controls, or even generate malicious code itself (OWASP, n.d.).

Vibe 编码依赖于自然语言提示。不幸的是，这为提示注入攻击打开了大门 （IBM， n.d.）。想象一下，一个恶意行为者精心制作了一个看似无辜的提示，但秘密地指示它LLM做一些有害的事情——比如泄露敏感数据、绕过安全控制，甚至自己生成恶意代码（OWASP，n.d.）。

It gets worse with indirect prompt injection. Malicious instructions could be hidden within data sources the AI accesses (like websites or documents), tricking the LLM without the user even typing a malicious prompt directly (CETAS, n.d.). Researchers have even shown how AI coding assistants can be compromised through seemingly innocuous configuration files, leading them to generate backdoored code (SC Magazine, 2025). It’s a subtle but potent threat vector, turning the AI’s helpful nature against itself.

间接及时注射会变得更糟。恶意指令可能隐藏在 AI 访问的数据源（如网站或文档）中，甚至LLM无需用户直接输入恶意提示即可对其进行欺骗（CETAS，日期不详）。研究人员甚至展示了 AI 编码助手如何通过看似无害的配置文件受到损害，导致它们生成后门代码（SC Magazine，2025 年）。它是一个微妙但强大的威胁载体，使 AI 的有用性质与自身背道而驰。

“Beware of Greeks bearing gifts… or seemingly innocent prompts hiding malicious intent.”

“当心希腊人携带礼物......或者看似无辜的提示隐藏了恶意。

Pro Tip: Sanitize and validate all inputs that might influence an LLM, especially data retrieved from external sources. Implement strict output encoding and context separation. Be wary of prompts that ask the AI to ignore previous instructions or perform actions outside its intended scope.

专业提示： 清理并验证所有可能影响 LLM 的输入，尤其是从外部源检索的数据。实施严格的输出编码和上下文分离。警惕要求 AI 忽略先前指令或执行超出其预期范围的作的提示。

Code Glitches & Gremlins: Reliability Takes a Hit

代码故障和小精灵：可靠性受到打击

Beyond security, the fundamental correctness and reliability of AI-generated code remain significant hurdles. Studies show LLMs frequently generate code that simply doesn’t work as intended or fails on complex tasks (Siddiq et al., 2024). AI-generated code has even been observed to be more prone to hangs and crashes compared to human-written equivalents (Zügner et al., n.d., as cited in Pearlmutter et al., 2024).

除了安全性之外，AI 生成的代码的基本正确性和可靠性仍然是重大障碍。研究表明，LLMs 经常生成的代码根本无法按预期工作或在复杂任务中失败（Siddiq 等人，2024 年）。甚至观察到，与人类编写的等效代码相比，AI 生成的代码更容易挂起和崩溃（Zügner 等人，日期不详，引自 Pearlmutter 等人，2024 年）。

Sometimes, the errors are embarrassingly simple — “stupid bugs” that a human developer would catch instantly, but the AI overlooks (Pearlmutter et al., 2024). Testing this code introduces new challenges, especially when dealing with incomplete snippets or novel AI-generated logic (Henley et al., 2024). Furthermore, AI might prioritize functional code over efficient code, potentially leading to performance regressions compared to human-optimized solutions (Shang et al., 2024). “It works” isn’t always the same as “it works well.”

有时，错误简单得令人尴尬——人类开发人员会立即捕捉到的“愚蠢错误”，但 AI 却忽略了（Pearlmutter 等人，2024 年）。测试此代码会带来新的挑战，尤其是在处理不完整的片段或新颖的 AI 生成逻辑时（Henley et al.， 2024）。此外，AI 可能会优先考虑功能代码而不是高效代码，与人工优化的解决方案相比，这可能会导致性能下降（Shang et al.， 2024）。“它有效”并不总是等同于“它运作良好”。

“It compiled! But will it run without tripping over its own digital feet?”

“它编译了！但它会在不被自己的数字脚绊倒的情况下运行吗？

Pro Tip: Implement comprehensive unit, integration, and regression testing suites for all code, especially AI-generated portions. Performance profiling is crucial if the AI-generated code is part of a performance-sensitive application. Don’t assume correctness.

专业提示： 为所有代码实施全面的单元、集成和回归测试套件， 尤其是 AI 生成的部分。如果 AI 生成的代码是性能敏感型应用程序的一部分，则性能分析至关重要。不要假设正确。

Safety Lapses: When Code Does Harm

安全失误：当代码造成危害时

The potential for harm extends beyond bugs and security flaws. AI systems, including those powering vibe coding, can be misused (intentionally or accidentally) to generate harmful content or code with dangerous implications (Safe Generative AI Workshop, n.d.). Think code designed for malicious purposes, or applications that, due to flawed logic, make harmful decisions in critical domains like healthcare or finance.

潜在的危害不仅限于 bug 和安全漏洞。AI 系统，包括为 vibe 编码提供支持的系统，可能会被滥用（有意或无意）生成有害内容或具有危险含义的代码（Safe Generative AI Workshop， n.d.）。想想为恶意目的而设计的代码，或者由于逻辑有缺陷而在医疗保健或金融等关键领域做出有害决策的应用程序。

A significant safety concern is overconfidence. Users might implicitly trust the AI’s output, deploying vibe-coded applications without the rigorous safety checks, ethical reviews, or human oversight they truly require (Safe Generative AI Workshop, n.d.). This misplaced trust can lead to unforeseen and damaging consequences when the AI’s limitations or biases surface in the real world.

一个重要的安全问题是过度自信 。用户可能会隐含地信任 AI 的输出，在没有他们真正需要的严格安全检查、道德审查或人工监督的情况下部署 vibe 编码的应用程序（Safe Generative AI Workshop， n.d.）。当 AI 的局限性或偏见在现实世界中浮现时，这种错位的信任可能会导致不可预见的破坏性后果。

“Trust, but verify… especially when the AI is playing doctor (or writing critical code).”

“信任，但要验证......尤其是当 AI 扮演医生（或编写关键代码）时。

Pro Tip: Establish clear protocols for human oversight and validation, particularly for applications generated or assisted by AI that operate in safety-critical domains. Define acceptable risk thresholds and ensure AI outputs are treated as suggestions, not infallible commands.

专业提示： 为人工监督和验证建立明确的协议，特别是对于由 AI 生成或协助且在安全关键领域运行的应用程序。定义可接受的风险阈值，并确保将 AI 输出视为建议，而不是万无一失的命令。

The Bias Blindspot: Code Reflecting Inequality

偏见盲点：反映不平等的代码

This is a critical Responsible AI challenge. LLMs learn from vast datasets reflecting our often biased world (Yuan et al., 2024). As a result, they can inadvertently learn, perpetuate, and even amplify societal biases related to gender, race, ethnicity, age, culture, and socioeconomic status (Mei et al., 2024; Yuan et al., 2024).

这是一项关键的负责任 AI 挑战。LLMs 从反映我们经常有偏见的世界的大量数据集中学习（Yuan et al.， 2024）。因此，他们可能会无意中学习、延续甚至放大与性别、种族、民族、年龄、文化和社会经济地位相关的社会偏见（Mei 等人，2024 年;Yuan et al.， 2024）。

Research has specifically uncovered demographic biases in code generation models (Sun et al., 2024). Training data often skews towards Western, Anglo-centric perspectives, potentially ignoring or misrepresenting diverse needs and contexts (Bhatt et al., 2024a; Bhatt et al., 2024b). When vibe coding relies on these biased models, it risks creating software that is unfair, discriminatory, or simply doesn’t work well for certain user groups. The “vibes” the AI picks up might be steeped in prejudice.

研究专门揭示了代码生成模型中的人口统计学偏见（Sun et al.， 2024）。训练数据通常偏向于西方的、以盎格鲁为中心的观点，可能会忽略或歪曲不同的需求和背景（Bhatt 等人，2024a;Bhatt 等人，2024b）。当 vibe 编码依赖于这些有偏见的模型时，它可能会创建不公平、歧视性或根本无法很好地用于某些用户组的软件。AI 捕捉到的 “共鸣” 可能充满了偏见。

“Garbage in, garbage out. Bias in data, bias in code. The scales aren’t always balanced.”

“垃圾进，垃圾出。数据中的偏差，代码中的偏差。天平并不总是平衡的。

Pro Tip: Actively audit AI coding tools and their outputs for potential biases. Advocate for and utilize models trained on diverse, representative datasets. Incorporate fairness testing and diverse user feedback throughout the development lifecycle when using AI-generated code.

专业提示： 积极审核 AI 编码工具及其输出是否存在潜在偏差。倡导并利用在各种具有代表性的数据集上训练的模型。使用 AI 生成的代码时，在整个开发生命周期中纳入公平性测试和不同的用户反馈。

3 Simple Rules for Smart Vibe Coding

Smart Vibe 编码的 3 个简单规则

1️⃣ Choose a Popular and Simple Stack

1️⃣ 选择一个流行且简单的堆栈

LLMs learn from the data they’re trained on.

LLMs从他们接受训练的数据中学习。

Their ability to generate correct, useful code is directly proportional to the quality and quantity of examples available online. The more common and well-documented the framework or language, the better the LLM’s performance.

他们生成正确、有用的代码的能力与在线可用示例的质量和数量成正比。框架或语言越常见且文档越齐全，LLM 性能就越好。

If you use obscure frameworks, niche languages, or highly custom setups, the LLM will have fewer examples to work from. This increases the chances of generating incorrect code.

如果您使用晦涩难懂的框架、小众语言或高度自定义的设置，则可供使用的示例LLM将较少。这会增加生成错误代码的可能性。

For example:  例如：

• Web Development: Stick to mainstream frameworks like React, Vue, Angular, Node.js (with Express), Python (with Django/Flask), Ruby on Rails, or PHP (with Laravel/Symfony).

• Web 开发 ：坚持使用主流框架，如 React、Vue、Angular、Node.js（使用 Express）、Python（使用 Django/Flask）、Ruby on Rails 或 PHP（使用 Laravel/Symfony）。

• Machine Learning / Data Science: Python is king here. Stick to well-documented libraries like Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, and standard visualization like Matplotlib or Seaborn.

• 机器学习/数据科学 ：Python 在这里是王道。坚持使用文档齐全的库，如 Pandas、NumPy、Scikit-learn、TensorFlow、PyTorch，以及标准可视化，如 Matplotlib 或 Seaborn。

  
  

2️⃣ Get Good at Git

2️⃣ 擅长 Git

When AI writes your code, it also gains the power to delete your working code without warning.

当 AI 编写您的代码时，它还能够在不发出警告的情况下删除您的工作代码。

And once it’s gone, good luck prompting it back into existence. That’s why version control become essential. Tools like Claude Code can even auto-generate commit messages to make versioning easier.

一旦它消失了，好运就会促使它重新出现。这就是为什么版本控制变得至关重要的原因。Claude Code 等工具甚至可以自动生成提交消息，以简化版本控制。

Here’s how to make the most of it:

以下是充分利用它的方法：

• Commit frequently: make small, atomic commits after each AI-generated snippet.

• 频繁提交 ：在每个 AI 生成的代码段之后进行小型原子提交。

• Write meaningful commit messages: be clear about what you’ve changed and why it’s important.

• 编写有意义的提交消息 ：清楚您更改了什么以及为什么它很重要。

• Use branches for experimentation: work with AI-generated code on a separate branch and merge only after proper review and testing.

• 使用分支进行实验 ：在单独的分支上使用 AI 生成的代码，并且只有在经过适当的审查和测试后才能合并。

  
  

3️⃣ Make Code Generation as Deterministic as Possible

3️⃣ 使代码生成尽可能具有确定性

Vibe coding doesn’t mean vague prompts, quite the opposite.

Vibe 编码并不意味着模糊的提示，恰恰相反。

It might sound counterintuitive, but you’ll get the best results when you are highly specific and structured in your requests. You shouldn’t rely on the AI’s creativity, you should guide its pattern-matching with clear instruction.

这听起来可能违反直觉，但当您的请求高度具体和结构化时，您将获得最佳结果。你不应该依赖 AI 的创造力，你应该用明确的指令来指导它的模式匹配。

Here’s how to do that:

以下是如何做到这一点：

• Break down problems: break tasks into smaller, defined steps and prompt the AI for each step individually.

• 分解问题 ：将任务分解为更小的、定义的步骤，并单独提示 AI 执行每个步骤。

• Use examples: show the AI what you’re looking for with input-output examples (few-shot prompting).

• 使用示例 ：通过输入输出示例（小样本提示）向 AI 展示您正在寻找的内容。

• Provide rich context: include relevant existing code snippets, data structures, API documentation, output formats, and constraints (e.g., “use functional components,” “avoid external libraries,” “handle errors for X”).

• 提供丰富的上下文 ：包括相关的现有代码片段、数据结构、API 文档、输出格式和约束（例如，“使用功能组件”、“避免使用外部库”、“处理 X 的错误”）。

  
  

4️⃣ (Bonus Tip) — Review and Test Carefully

4️⃣ （Bonus Tip） — 仔细审查和测试

Never assume AI-generated code is flawless, always treat it as a draft.

永远不要假设 AI 生成的代码是完美的，而是始终将其视为草稿。

The AI doesn’t understand the code it writes. It might look right on the surface but still contain errors, security holes, or performance issues.

AI 不理解它编写的代码。它可能表面上看起来不错，但仍然包含错误、安全漏洞或性能问题。

Here’s how to review AI-generated code:

以下是查看 AI 生成的代码的方法：

• Understand any line: Before committing any AI-generated code, read it carefully. Do you understand what every line does? If not, ask the AI to explain it or research it yourself.

• 理解任何行 ：在提交任何 AI 生成的代码之前，请仔细阅读。你明白每行的作用吗？如果没有，请让 AI 解释或自己研究。

• Test rigorously: Use unit tests and run manual checks to confirm the code behaves as expected under various conditions, including edge cases. AI can help write tests, but manual testing oversight is crucial to ensure nothing slips through.

• 严格测试 ：使用单元测试并运行手动检查，以确认代码在各种条件（包括边缘情况）下的行为符合预期。AI 可以帮助编写测试，但手动测试监督对于确保万无一失至关重要。

  
  

The Bottom Line: AI as Copilot, Not Autopilot

底线：AI 是 Copilot，而不是 Autopilot

Vibe coding as hyped by AI influencers only works for trivial tasks or throwaway prototypes. Relying entirely on AI without understanding the code is risky and irresponsible.

AI 影响者大肆宣传的 Vibe 编码仅适用于琐碎的任务或一次性原型。完全依赖 AI 而不了解代码是有风险且不负责任的。

That said, AI can still be useful, especially for:

也就是说，AI 仍然很有用，尤其是对于：

• Reducing boilerplate 

• 减少样板

• Accelerating coding tasks

• 加速编码任务

• Troubleshooting specific coding issues

• 排查特定编码问题

• Learning new languages or frameworks

• 学习新语言或框架

  
  

The future doesn’t belong to hose who reject AI, nor to those who blindly trust it. The most successful developers will be those who combine human insight with the efficiency of AI.

未来不属于拒绝 AI 的人，也不属于盲目信任 AI 的人。最成功的开发人员将是那些将人类洞察力与 AI 效率相结合的开发人员。

Use AI as a tool, but don’t abdicate your responsibility for the code:

使用 AI 作为工具，但不要放弃您对代码的责任：

• Stick to familiar stacks  

• 坚持使用熟悉的堆栈

• Master Git version control

• 主 Git 版本控制

• Make AI as deterministic as possible

• 使 AI 尽可能具有确定性

• And always verify and test the results

• 并始终验证和测试结果

  
  

That’s how you get the most from AI-coding without breaking things.

这就是您如何在不破坏事物的情况下从 AI 编码中获得最大收益。

Ripples in the Pond: Societal Shifts and Shakes

池塘中的涟漪：社会变迁和震动

The rise of vibe coding isn’t happening in a vacuum. Its widespread adoption could send significant ripples across society, reshaping jobs, ethics, and even how we think.

氛围编码的兴起并不是在真空中发生的。它的广泛采用可能会在整个社会中掀起巨大的涟漪，重塑工作、道德，甚至我们的思维方式。

The Evolving Code-scape: Jobs and Roles in Flux

不断发展的代码景观：Flux 中的工作和角色

Let’s address the elephant in the room: jobs. Yes, AI code generation will automate tasks currently done by developers (Tommie Experts, 2025). Estimates vary wildly, but the potential for disruption is real (AIPRM, n.d.; Exploding Topics, 2024). However, the narrative isn’t purely one of replacement. New roles are emerging — AI trainers, prompt engineers, AI ethicists, specialists in managing AI-driven development (Tommie Experts, 2025; Simbla, n.d.).

让我们来谈谈房间里的大象：工作。是的，AI 代码生成将自动执行当前由开发人员完成的任务（Tommie Experts，2025 年）。估计差异很大，但潜在的破坏是真实的（AIPRM， n.d.;爆炸主题，2024 年）。然而，这种叙述并不纯粹是替代的叙述。新的角色正在出现——AI 培训师、提示工程师、AI 伦理学家、管理 AI 驱动开发的专家（Tommie Experts，2025 年;Simbla， n.d.）。

The traditional programmer role is likely shifting towards orchestration and oversight — guiding AI agents, validating their output, focusing on high-level architecture and complex problem-solving (Iyer et al., 2024). Productivity boosts might lead to smaller, more specialized teams (Reddit user comment, as cited in Reddit, n.d.-c). Demand for AI-specific skills (data science, ML engineering) is skyrocketing (Brainhub, 2024). The squeeze might be felt most acutely at the junior end, where tasks are more easily automated (Adnovum, 2025). Adaptability and continuous learning will be key.

传统的程序员角色可能会转向编排和监督——指导 AI 代理，验证其输出，专注于高级架构和复杂问题的解决（Iyer et al.， 2024）。生产力的提高可能会导致更小、更专业的团队（Reddit 用户评论，如 Reddit 中引用的，n.d.-c）。对 AI 特定技能（数据科学、ML 工程）的需求正在飙升（Brainhub，2024 年）。这种挤压可能在初级端最明显，因为那里的任务更容易自动化（Adnovum，2025 年）。适应性和持续学习将是关键。

“New tools, new rules, new roles. The developer of tomorrow might look more like a conductor.”

“新工具、新规则、新角色。未来的开发商可能看起来更像一个指挥家。

Pro Tip: Embrace lifelong learning. Focus on developing skills that complement AI, such as critical thinking, complex problem-solving, system design, domain expertise, and understanding AI/ML principles. Learn how to effectively use AI tools as force multipliers.

专业提示： 拥抱终身学习。专注于培养与 AI 相辅相成的技能，例如批判性思维、复杂问题解决、系统设计、领域专业知识和理解 AI/ML 原则。了解如何有效地使用 AI 工具作为力量倍增器。

The Bigger Picture: Ethics, Accountability, and Trust

更大的图景：道德、问责制和信任

Beyond jobs, widespread vibe coding forces us to confront broader societal questions. How do we handle the ethical implications when AI, lacking true understanding of legal or moral norms, generates code used in sensitive contexts (Pearlmutter et al., 2024)? Who is accountable when vibe-coded software fails, causes harm, or exhibits bias (Adnovum, 2025)?

除了工作之外，广泛的氛围编码迫使我们面对更广泛的社会问题。当人工智能缺乏对法律或道德规范的真正理解，生成用于敏感环境的代码时，我们如何处理道德影响（Pearlmutter et al.， 2024）？当 vibe 编码软件出现故障、造成伤害或表现出偏见时，谁负责（Adnovum，2025 年）？

There’s also the risk of malicious actors leveraging the ease of vibe coding to create harmful software more rapidly (CRA, 2024). Furthermore, as more content (including code) becomes AI-generated, how do we maintain trust and authenticity in the digital realm (Bhatt et al., 2024b)? Establishing clear ethical guidelines, governance frameworks, and accountability structures for AI in coding is no longer optional; it’s essential for navigating this new territory responsibly (Pearlmutter et al., 2024; Adnovum, 2025).

恶意行为者还存在利用 vibe 编码的便利性更快地创建有害软件的风险（CRA，2024 年）。此外，随着越来越多的内容（包括代码）成为 AI 生成的，我们如何在数字领域保持信任和真实性（Bhatt et al.， 2024b）？为编码中的 AI 建立明确的道德准则、治理框架和问责结构不再是可有可无的;这对于负责任地驾驭这个新领域至关重要（Pearlmutter 等人，2024 年;Adnovum，2025 年）。

“Code is law (sometimes literally). Who judges the AI when the code goes wrong?”

“代码就是法律（有时是字面意思）。当代码出错时，谁来评判 AI？

Pro Tip: Advocate for and contribute to the development of industry standards and best practices for responsible AI development and deployment, including clear guidelines for using AI code generation tools. Promote transparency in how AI is used in software creation.

专业提示： 倡导并促进负责任的 AI 开发和部署的行业标准和最佳实践的制定，包括使用 AI 代码生成工具的明确指导方针。提高 AI 在软件创建中的使用方式的透明度。

Our Brains on AI: Cognitive Shifts and Learning Challenges

我们的大脑对 AI 的影响：认知转变和学习挑战

Finally, what does relying on AI for cognitive heavy lifting like coding do to our own brains? While AI can reduce cognitive load (Microsoft Dev Blogs, 2025), there’s a valid concern about cognitive offloading — delegating thinking tasks to the point where our own skills atrophy (Schaefer Marketing Solutions, 2025; Kazemitabaar et al., 2025a). If we always let the AI solve the problem, do we forget how to solve it ourselves?

最后，依靠 AI 进行编码等认知繁重的工作对我们自己的大脑有什么影响？虽然 AI 可以减少认知负荷（Microsoft Dev Blogs，2025 年），但对认知卸载的担忧是合理的——将思考任务委派到我们自己的技能萎缩的程度（Schaefer Marketing Solutions，2025 年;Kazemitabaar 等人，2025a）。如果我们总是让 AI 解决问题，我们是不是忘记了自己怎么解决呢？

In education, the ease of generating solutions via vibe coding might create an illusion of understanding. Students might get the right answer without engaging deeply with the underlying principles, potentially hindering long-term learning and critical thinking development (Kazemitabaar et al., 2024; Kazemitabaar et al., 2025b). Studies suggest students might overestimate their learning when using AI aids and struggle when those aids are removed (Kazemitabaar et al., 2024). Younger learners might become particularly reliant, impacting their fundamental skill acquisition (Kazemitabaar et al., 2024). Balancing AI assistance with foundational learning is a pedagogical tightrope walk.

在教育领域，通过 vibe 编码生成解决方案的便利性可能会产生一种理解的错觉 。学生在没有深入参与基本原则的情况下可能会得到正确的答案，这可能会阻碍长期学习和批判性思维的发展（Kazemitabaar 等人，2024 年;Kazemitabaar et al.， 2025b）。研究表明，学生在使用人工智能辅助工具时可能会高估自己的学习效果，而当这些辅助工具被移除时可能会很挣扎（Kazemitabaar et al.， 2024）。年轻的学习者可能会变得特别依赖，从而影响他们的基本技能习得（Kazemitabaar et al.， 2024）。平衡 AI 辅助与基础学习是一条教学钢丝。

“Use it or lose it? The cognitive cost of letting AI do all the thinking.”

“用还是丢？让 AI 完成所有思考的认知成本。

Pro Tip for Educators & Learners: Use AI coding tools as scaffolds, not crutches. Focus on understanding the why behind the AI’s output. Encourage manual problem-solving and code-reading alongside AI generation. Design learning activities that require critical evaluation and modification of AI-generated code.

教育工作者和学习者的专业提示： 使用 AI 编码工具作为脚手架，而不是拐杖。专注于了解 AI 输出背后的原因 。鼓励在生成 AI 的同时手动解决问题和阅读代码。设计需要对 AI 生成的代码进行严格评估和修改的学习活动。

Conclusion — Charting a Responsible Vibe Forward

结论 — 制定负责任的氛围

So, vibe coding. Is it the promised land of effortless creation or a minefield of unintended consequences? The truth, as is often the case, lies somewhere in the messy middle.

所以，氛围编码。它是轻松创造的应许之地，还是意外后果的雷区？正如通常的情况一样，真相位于混乱的中间地带。

The potential is undeniable: faster development, democratized access, enhanced creativity, and a shift towards higher-level problem-solving. Vibe coding could genuinely revolutionize how we build software, empowering more people to bring their digital ideas to life (Replit, n.d.-b; Gitpod, 2025).

潜力是不可否认的：更快的发展、民主化的访问、增强的创造力以及向更高层次的问题解决方式的转变。Vibe 编码可以真正彻底改变我们构建软件的方式，让更多人能够将他们的数字想法变为现实（Replit， n.d.-b;Gitpod，2025 年）。

However, the risks are equally real and demand our urgent attention. The specter of insecure code, the insidious threat of prompt injection, the propagation of bias, the questions around reliability, safety, ethics, job shifts, and cognitive impact — these are not minor quibbles. They are fundamental challenges we must address head-on (Security Journey, 2025; CSET, 2025a; Yuan et al., 2024; Pearlmutter et al., 2024).

然而，风险同样真实存在，需要我们紧急关注。不安全代码的幽灵、及时注入的阴险威胁、偏见的传播、围绕可靠性、安全性、道德、工作变动和认知影响的问题——这些都是不小的狡辩。它们是我们必须正面应对的基本挑战（Security Journey，2025 年;CSET，2025a;Yuan et al.， 2024;Pearlmutter et al.， 2024）。

Ignoring these issues while blindly chasing the “vibe” would be irresponsible, potentially leading to systems that are brittle, unfair, unsafe, and ultimately, untrustworthy. We can’t afford to just “Prompt It, Got It,” and then later “Regret It.”

忽视这些问题而盲目追逐 “氛围” 是不负责任的，可能会导致系统脆弱、不公平、不安全，并最终不值得信赖。我们不能只说 “Prompt It， Got It” 然后又说 “Regret It”。

Recommendations: Coding the Future, Responsibly

建议：负责任地编码未来

To navigate this complex landscape and harness vibe coding’s potential for good, we need a concerted effort grounded in responsibility:

为了驾驭这一复杂的环境并利用 Vibe 编码的潜力，我们需要以责任为基础的共同努力：

• Security & Safety First: Mandate rigorous testing, validation, and security scanning specifically tailored for AI-generated code (SecureFlag, 2024; Pearlmutter et al., 2024). Develop and share best practices for secure prompting and integrating AI code safely.

• 安全第一： 强制要求专门为 AI 生成的代码量身定制严格的测试、验证和安全扫描（SecureFlag，2024 年;Pearlmutter et al.， 2024）。开发和共享安全提示和集成 AI 代码的最佳实践。

• Bias Beware: Invest heavily in detecting and mitigating bias in code-generating models (Sun et al., 2024). Prioritize diverse training data and establish robust ethical frameworks for fairness in AI-driven development.

• 偏见当心： 大力投资检测和减轻代码生成模型中的偏差（Sun et al.， 2024）。优先考虑多样化的训练数据，并建立强大的道德框架，以实现 AI 驱动型开发的公平性。

• Education & Critical Thinking: Foster educational approaches that use AI tools to enhance understanding, not replace it (Replit, n.d.-b). Emphasize code review, fundamental principles, and the critical thinking needed to evaluate AI output (Kazemitabaar et al., 2024).

• 教育与批判性思维： 培养使用 AI 工具来增强理解的教育方法，而不是取代它（Replit，n.d.-b）。强调代码审查、基本原则和评估 AI 输出所需的批判性思维（Kazemitabaar 等人，2024 年）。

• Ethical Guardrails & Accountability: Develop clear industry standards, ethical guidelines, and legal frameworks defining ownership, liability, and responsible use for AI-generated code (Adnovum, 2025; Pearlmutter et al., 2024).

• 道德护栏和问责制： 制定明确的行业标准、道德准则和法律框架，定义 AI 生成代码的所有权、责任和负责任的使用（Adnovum，2025 年;Pearlmutter et al.， 2024）。

• Study the Long Game: Support ongoing research into the long-term societal, economic, and cognitive impacts of AI-assisted coding to inform adaptive strategies (Kazemitabaar et al., 2024).

• 研究长期游戏： 支持对 AI 辅助编码的长期社会、经济和认知影响的持续研究，为适应性策略提供信息（Kazemitabaar 等人，2024 年）。

• Human-in-the-Loop, Always: Champion a collaborative model where AI augments human capabilities, rather than aiming for full replacement (Adnovum, 2025). Keep humans firmly in control, especially for critical decisions and validation.

• 人机协同，始终： 倡导一种协作模式，让 AI 增强人类的能力，而不是以完全取代为目标（Adnovum，2025 年）。让人类牢牢掌控，尤其是在关键决策和验证方面。

  
  

The future of software development will involve AI. Trends like vibe coding are powerful indicators of that shift. But the quality of that future — whether it’s secure, equitable, reliable, and ultimately beneficial — depends on the choices we make now. Let’s ensure we’re coding not just with vibes, but with wisdom, foresight, and a deep sense of responsibility.

软件开发的未来将涉及 AI。氛围编码等趋势是这种转变的有力指标。但是，这个未来的质量 ——是否安全、公平、可靠以及最终有益——取决于我们现在做出的选择。让我们确保我们的编码不仅具有共鸣，而且具有智慧、远见和深厚的责任感。

References

Defining Vibe Coding & Core Concepts

定义氛围编码和核心概念

1. Bitget News. (2025, March 31). Is crypto the ultimate vibe coding industry? Retrieved March 31, 2025, from https://www.bitget.com/news/detail/12560604668135

2. Gitpod. (2025, March 31). ‘Vibe coding’ is a revolution for optimistic creatives. Gitpod Blog. Retrieved March 31, 2025, from https://www.gitpod.io/blog/vibe-coding

3. McNulty, N. (2025, March 31). Vibe Coding. AI-Assisted Coding for Non-Developers. Medium. Retrieved March 31, 2025, from https://medium.com/@niall.mcnulty/vibe-coding-b79a6d3f0caa (Cites DEV Community post by erikch: https://dev.to/erikch/what-i-learned-vibe-coding-30em)

4. Reddit. (n.d.-a). What is vibe coding? [Online forum post]. Reddit. Retrieved March 31, 2025, from https://www.reddit.com/r/Bard/comments/1jn2v3f/what_is_vibe_coding/

5. Replit. (n.d.-a). What is Vibe Coding? Replit Blog. Retrieved March 31, 2025, from https://blog.replit.com/what-is-vibe-coding

6. Replit. (n.d.-b). What is Vibe Coding? Replit Blog. Retrieved March 31, 2025, from https://blog.replit.com/what-is-vibe-coding#:~:text=Vibe%20coding%20means%20leaning%20on,getting%20stuck%20in%20technical%20details. (Note: Specific fragment link provided in source list)

7. Vibe coding. (n.d.). In Wikipedia. Retrieved March 31, 2025, from https://en.wikipedia.org/wiki/Vibe_coding

8. Willison, S. (2025, March 19). Not all AI-assisted programming is vibe coding (but vibe coding rocks). Simon Willison’s Weblog. Retrieved March 31, 2025, from https://simonwillison.net/2025/Mar/19/vibe-coding/

   
   

Applications, Benefits & Positive Perspectives

应用、好处和积极的观点

1. Gitpod. (2025, March 31). ‘Vibe coding’ is a revolution for optimistic creatives. Gitpod Blog. Retrieved March 31, 2025, from https://www.gitpod.io/blog/vibe-coding

2. Replit. (n.d.-b). What is Vibe Coding? Replit Blog. Retrieved March 31, 2025, from https://blog.replit.com/what-is-vibe-coding

3. Security Journey. (2025, March 31). 10 Professional Developers on the True Promise and Peril of Vibe Coding. Retrieved March 31, 2025, from https://www.securityjourney.com/post/10-professional-developers-on-the-true-promise-and-peril-of-vibe-coding

   
   

Concerns, Criticisms & Potential Drawbacks

关注、批评和可能的缺点

1. Adnovum. (2025, March 31). Will AI Replace Software Engineers? Unveiling the Truth in 2024. Adnovum Blog. Retrieved March 31, 2025, from https://www.adnovum.com/blog/will-ai-replace-software-engineers

2. Cendyne.dev. (2025, March 19). “Vibe Coding” vs Reality. Retrieved March 31, 2025, from https://cendyne.dev/posts/2025-03-19-vibe-coding-vs-reality.html

3. Legit Security. (2025, March 31). AI Code Generation: The Risks and Benefits of AI in Software. Retrieved March 31, 2025, from https://www.legitsecurity.com/blog/ai-code-generation-benefits-and-risks

4. McNulty, N. (2025, March 31). Vibe Coding. AI-Assisted Coding for Non-Developers. Medium. Retrieved March 31, 2025, from https://medium.com/@niall.mcnulty/vibe-coding-b79a6d3f0caa (Cites DEV Community post by erikch: https://dev.to/erikch/what-i-learned-vibe-coding-30em)

5. Pearlmutter, B. A., Johnson, R., & Morgan, K. (2024). Artificial-Intelligence Generated Code Considered Harmful: A Road Map for Secure and High-Quality Code Generation. ResearchGate. https://www.researchgate.net/publication/384502842_Artificial-Intelligence_Generated_Code_Considered_Harmful_A_Road_Map_for_Secure_and_High-Quality_Code_Generation (Note: Source provided as PDF link, details extracted) (Also appears as arXiv:2408.10554)

6. Pearlmutter, B. A., Johnson, R., & Morgan, K. (2024). Artificial-Intelligence generated code considered harmful: A road map for secure and high-quality code generation. arXiv preprint arXiv:2408.10554v2. https://arxiv.org/pdf/2408.10554

7. Reddit. (n.d.-b). Karpathy’s ‘Vibe Coding’ Movement Considered Harmful [Online forum post]. Reddit. Retrieved March 31, 2025, from https://www.reddit.com/r/programming/comments/1jms5sv/karpathys_vibe_coding_movement_considered_harmful/

8. Security Journey. (2025, March 31). 10 Professional Developers on the True Promise and Peril of Vibe Coding. Retrieved March 31, 2025, from https://www.securityjourney.com/post/10-professional-developers-on-the-true-promise-and-peril-of-vibe-coding

9. Siddiq, M. L., Santos, J., Chaparro, O., Lopes, C., & Tan, L. (2024). Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise. arXiv preprint arXiv:2412.06603v2. https://arxiv.org/html/2412.06603v2

10. Vibe coding. (n.d.). In Wikipedia. Retrieved March 31, 2025, from https://en.wikipedia.org/wiki/Vibe_coding

    
    

Responsible AI: Security Vulnerabilities & Risks

负责任的人工智能：安全漏洞和风险

1. All Things Open. (2025, March 31). 6 limitations of AI code assistants and why developers should be cautious. Retrieved March 31, 2025, from https://allthingsopen.org/articles/ai-code-assistants-limitations

2. Center for Security and Emerging Technology (CSET). (2025a, March 31). Cybersecurity Risks of AI-Generated Code. Retrieved March 31, 2025, from https://cset.georgetown.edu/publication/cybersecurity-risks-of-ai-generated-code/

3. Center for Security and Emerging Technology (CSET). (2025b, March 31). Cybersecurity Risks of AI-Generated Code [PDF]. Retrieved March 31, 2025, from https://cset.georgetown.edu/wp-content/uploads/CSET-Cybersecurity-Risks-of-AI-Generated-Code.pdf

4. ITPro. (2025, March 31). AI-generated code risks: What CISOs need to know. Retrieved March 31, 2025, from https://www.itpro.com/technology/artificial-intelligence/ai-generated-code-risks-what-cisos-need-to-know

5. Pearlmutter, B. A., Johnson, R., & Morgan, K. (2024). Artificial-Intelligence Generated Code Considered Harmful: A Road Map for Secure and High-Quality Code Generation. ResearchGate. https://www.researchgate.net/publication/384502842_Artificial-Intelligence_Generated_Code_Considered_Harmful_A_Road_Map_for_Secure_and_High-Quality_Code_Generation

6. SecureFlag. (2024, October 16). The risks of generative AI coding in software development. SecureFlag Blog. Retrieved March 31, 2025, from https://blog.secureflag.com/2024/10/16/the-risks-of-generative-ai-coding-in-software-development/

7. Siddiq, M. L., Santos, J., Chaparro, O., Lopes, C., & Tan, L. (2024). A systematic literature review on the impact of AI models on the security of code generation. PLoS ONE, 19(5), e0302848. https://doi.org/10.1371/journal.pone.0302848 (Note: Source 28 inferred as this based on content/context)

   
   

Responsible AI: Prompt Injection & System Manipulation

负责任的人工智能：及时注射和系统控

1. Center for the Analysis of Targeted Attacks (CETAS). (n.d.). Indirect Prompt Injection: Generative AI’s Greatest Security Flaw. The Alan Turing Institute. Retrieved March 31, 2025, from https://cetas.turing.ac.uk/publications/indirect-prompt-injection-generative-ais-greatest-security-flaw

2. IBM. (n.d.). What Is a Prompt Injection Attack? IBM Think. Retrieved March 31, 2025, from https://www.ibm.com/think/topics/prompt-injection

3. OWASP Foundation. (n.d.). LLM01:2025 Prompt Injection. OWASP Top 10 for LLM & Generative AI Security. Retrieved March 31, 2025, from https://genai.owasp.org/llmrisk/llm01-prompt-injection/

4. SC Media. (2025, March 31). How AI coding assistants could be compromised via rules file. Retrieved March 31, 2025, from http://www.scmagazine.com/news/how-ai-coding-assistants-could-be-compromised-via-rules-file

5. Zhang, Y., et al. (2024). DeVAIC: A Tool for Security Assessment of AI-generated Code. arXiv preprint arXiv:2404.07548v2. https://arxiv.org/html/2404.07548v2 (Note: Source 29 inferred based on content)

   
   

Responsible AI: Code Errors, Reliability & Testing

负责任的人工智能：代码错误、可靠性和测试

1. Henley, A. Z., Kazemitabaar, M., Hughes, M. C., & Van Meter, P. (2024). Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning. arXiv preprint arXiv:2410.08922v1. https://arxiv.org/html/2410.08922v1 (Note: Source 48 points here)

2. Pearlmutter, B. A., Johnson, R., & Morgan, K. (2024). Artificial-Intelligence Generated Code Considered Harmful: A Road Map for Secure and High-Quality Code Generation. ResearchGate. https://www.researchgate.net/publication/384502842_Artificial-Intelligence_Generated_Code_Considered_Harmful_A_Road_Map_for_Secure_and_High-Quality_Code_Generation

3. Shang, W., Zhou, X., Xia, X., Lo, D., & Hassan, A. E. (2024). Assessing the Performance of AI-Generated Code: A Case Study on GitHub Copilot. University of Waterloo ECE. Retrieved March 31, 2025, from https://ece.uwaterloo.ca/~wshang/pubs/ISSRE_2024 (Note: Full reference details inferred)

4. Siddiq, M. L., et al. (2024). Are Large Language Models the End of Programming? arXiv preprint arXiv:2409.19182v2. https://arxiv.org/pdf/2409.19182 (Note: Source 13 points here)

5. Zügner, D., Parshin, D., & Günnemann, S. (n.d.). LLM vs. Human: An Empirical Study of the Reliability of Code Generated by Large Language Models. arXiv. (Note: Inferred reference based on citation in Pearlmutter et al., 2024; specific arXiv ID not provided in source text)

   
   

Responsible AI: Safety & Harm Potential

负责任的人工智能：安全与潜在危害

1. Safe Generative AI Workshop. (n.d.). Retrieved March 31, 2025, from https://safegenaiworkshop.github.io/

   
   

   Responsible AI: Bias & Fairness
   

负责任的人工智能：偏见与公平

1. Bhatt, U., et al. (2024a). Diverse Yet Biased: Towards Mitigating Biases in Generative AI (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(22), 24790–24791. https://ojs.aaai.org/index.php/AAAI/article/view/30512/32655

2. Bhatt, U., et al. (2024b). Diverse Yet Biased: Towards Mitigating Biases in Generative AI (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(22). https://ojs.aaai.org/index.php/AAAI/article/view/30512

3. Mei, J., et al. (2024). Bias in Large Language Models: Origin, Evaluation, and Mitigation. arXiv preprint arXiv:2411.10915v1. https://arxiv.org/html/2411.10915v1

4. Sun, Z., et al. (2024). Measuring and Mitigating Demographic Bias in Code Generation Models. OpenReview. Retrieved March 31, 2025, from https://openreview.net/pdf?id=BOP5McdqGy

5. Yuan, L., et al. (2024). Bias and Fairness in Large Language Models: A Survey. Computational Linguistics, 50(3), 1097–1186. https://direct.mit.edu/coli/article/50/3/1097/121961/Bias-and-Fairness-in-Large-Language-Models-A

   
   

Societal Impacts: Job Market & Developer Roles

社会影响：就业市场和开发者角色

1. Adnovum. (2025, March 31). Will AI Replace Software Engineers? Unveiling the Truth in 2024. Adnovum Blog. Retrieved March 31, 2025, from https://www.adnovum.com/blog/will-ai-replace-software-engineers

2. AIPRM. (n.d.). 50+ AI Replacing Jobs Statistics 2024. Retrieved March 31, 2025, from https://www.aiprm.com/ai-replacing-jobs-statistics/

3. Brainhub. (2024). Is There a Future for Software Engineers? The Impact of AI [2024]. Brainhub Library. Retrieved March 31, 2025, from https://brainhub.eu/library/software-developer-age-of-ai

4. Exploding Topics. (2024). 60+ Stats On AI Replacing Jobs (2024). Retrieved March 31, 2025, from https://explodingtopics.com/blog/ai-replacing-jobs

5. Iyer, S., et al. (2024). From Today’s Code to Tomorrow’s Symphony: The AI Transformation of Developer’s Routine by 2030. arXiv preprint arXiv:2405.12731. https://arxiv.org/pdf/2405.12731

6. Kim, J., et al. (2024). GENERATIVE AI IMPACT ON LABOR MARKET: ANALYZING CHATGPT’S DEMAND IN JOB ADVERTISEMENTS. arXiv preprint arXiv:2412.07042. https://arxiv.org/pdf/2412.07042 (Note: Source 50 points here)

7. Reddit. (n.d.-c). AI effects on the job market [Online forum comment]. Reddit. Retrieved March 31, 2025, from https://www.reddit.com/r/computerscience/comments/197bsau/ai_effects_on_the_job_market/

8. Simbla. (n.d.). AI-and-Employment-Revolution:-Navigating-the-Impact-of-Artificial-Intelligence-on-the-Job-Market. Simbla Blog. Retrieved March 31, 2025, from https://www.simbla.com/post/ai-and-employment-revolution:-navigating-the-impact-of-artificial-intelligence-on-the-job-market

9. Tommie Experts. (2025, March 31). Generative AI’s Real-World Impact on Job Markets. University of St. Thomas Newsroom. Retrieved March 31, 2025, from https://news.stthomas.edu/generative-ais-real-world-impact-on-job-markets/

   
   

Societal Impacts: Broader Ethics, Accountability & Trust

社会影响：更广泛的道德、问责制和信任

1. Adnovum. (2025, March 31). Will AI Replace Software Engineers? Unveiling the Truth in 2024. Adnovum Blog. Retrieved March 31, 2025, from https://www.adnovum.com/blog/will-ai-replace-software-engineers

2. Bhatt, U., et al. (2024b). Diverse Yet Biased: Towards Mitigating Biases in Generative AI (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(22). https://ojs.aaai.org/index.php/AAAI/article/view/30512

3. Computing Research Association (CRA). (2024, October). The Security Risks of Generative AI: From Identification and Mitigation to Responsible Use. CRA News. Retrieved March 31, 2025, from https://cra.org/crn/2024/10/the-security-risks-of-generative-ai-from-identification-and-mitigation-to-responsible-use/

4. Pearlmutter, B. A., Johnson, R., & Morgan, K. (2024). Artificial-Intelligence generated code considered harmful: A road map for secure and high-quality code generation. arXiv preprint arXiv:2408.10554v2. https://arxiv.org/pdf/2408.10554

   
   

Societal Impacts: Cognitive Development & Learning

社会影响：认知发展与学习

1. Kazemitabaar, M. A., et al. (2024). Protecting Human Cognition in the Age of AI. arXiv preprint arXiv:2502.12447v1. https://arxiv.org/html/2502.12447v1

2. Kazemitabaar, M. A., et al. (2025a). Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning. Austin Z. Henley. Retrieved March 31, 2025, from https://austinhenley.com/pubs/Kazemitabaar2025IUI_AIFriction.pdf

3. Kazemitabaar, M. A., Henley, A. Z., Hughes, M. C., & Van Meter, P. (2025b). Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning. Proceedings of the 30th International Conference on Intelligent User Interfaces (IUI ‘25). ACM. (Note: Source 48/49 details combined and inferred conference) (Also appears as arXiv:2410.08922)

4. Microsoft Dev Blogs. (2025, March 31). How does generative AI impact Developer Experience? Premier Developer Blog. Retrieved March 31, 2025, from https://devblogs.microsoft.com/premier-developer/how-does-generative-ai-impact-developer-experience/

5. Schaefer Marketing Solutions. (2025, February 12). How frequent AI usage is leading to cognitive decline. BusinessesGrow.com. Retrieved March 31, 2025, from https://businessesgrow.com/2025/02/12/cognitive-decline/