## 氛围编码是如何扼杀开源的
How Vibe Coding Is Killing Open Source

原始链接: https://hackaday.com/2026/02/02/how-vibe-coding-is-killing-open-source/

一项最新研究表明,使用像GitHub Copilot这样的LLM聊天机器人*生成*代码的“氛围编程”对开源生态系统构成重大威胁。核心问题在于开发者与开源项目的直接互动减少。开发者不再理解和参与库的使用,而是依赖于聊天机器人的输出,可能偏向于LLM训练数据中流行的代码,而非*最佳*代码。 这会减少项目网站的流量,阻碍资金和社区发展,类似于对Spotify等平台上艺术家报酬的担忧。此外,LLM不会回馈它们所使用的项目——没有错误报告,没有社区参与。 早期数据表明,“氛围编程”并没有显著提高生产力,甚至可能*增加*错误并降低开发人员的技能。虽然人工智能技术本身并非负面,但该研究警告说,将开发委托给LLM最终可能会扼杀创新,并损害对开源至关重要的协作精神,尤其是在JavaScript和Python等生态系统中。

## 氛围编码与开源的未来 一篇 Hacker News 的讨论探讨了使用像 Claude Code 这样的 LLM 快速生成代码的“氛围编码”是否对开源有害。虽然一些人认为它无法扼杀开源的*理念*,但人们对它的实际影响表示担忧。 用户指出,对现有项目的*上游*贡献有所下降,因为个人越来越多地选择生成定制的解决方案来满足他们的需求。这减少了对共享库和它们所代表的协作“公共资源”的依赖。人们也担心来自 LLM 使用的低质量贡献(冗长的 PR,虚假问题)。 然而,其他人认为这只是提高了开源项目的门槛,需要更大的差异化才能吸引用户。对于较小的工具,个性化、LLM 生成的代码的便利性超过了采用现有、更复杂的解决方案的好处。这场讨论强调了一种潜在的焦点转变——从软件作为完成的*产品*,转向利用基础的开源基础设施。
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原文

Does vibe coding risk destroying the Open Source ecosystem? According to a pre-print paper by a number of high-profile researchers, this might indeed be the case based on observed patterns and some modelling. Their warnings mostly center around the way that user interaction is pulled away from OSS projects, while also making starting a new OSS project significantly harder.

“Vibe coding” here is defined as software development that is assisted by an LLM-backed chatbot, where the developer asks the chatbot to effectively write the code for them. Arguably this turns the developer into more of a customer/client of the chatbot, with no requirement for the former to understand what the latter’s code does, just that what is generated does the thing that the chatbot was asked to create.

This also removes the typical more organic selection process of libraries and tooling, replacing it with whatever was most prevalent in the LLM’s training data. Even for popular projects visits to their website decrease as downloads and documentation are replaced by LLM chatbot interactions, reducing the possibility of promoting commercial plans, sponsorships, and community forums. Much of this is also reflected in the plummet in usage of community forums like Stack Overflow.

(Credit: Koren et al., 2026)
(Credit: Koren et al., 2026)

If we consider this effect of ‘AI-assisted’ software development to be effectively the delegating of the actual engineering and development to the statistical model of an LLM, then it’s easy to see the problems here. The LLM will not interact with the developers of a library or tool, nor submit usable bug reports, or be aware of any potential issues no matter how well-documented.

Although the authors of this paper are still proponents of ‘AI technology’, their worries seem well-warranted, even if it’s unclear at this point how big the impact is going to be. Software ecosystems like those involving JavaScript, Python, and web technologies are likely to suffer the impact from vibe coding first, as their audiences appear to be more into such vibes, and the training sets were largest.

It’s also a topic that is highly controversial, ever since Microsoft launched GitHub Copilot in 2021. Since then we saw reports in 2024 that ‘vibe coding’ using Copilot and similar chatbots offered no real benefits unless adding 41% more bugs is a measure of success.

By the time we hit 2025, we can observe an even more negative mood, with LLM chatbots in general being accused of degrading the cognitive skills of those using them, vibe coding chatbots reducing productivity by 19%, and experienced developers who gave them a whirl subsequently burning them to the ground in scathing reviews.

All of which reinforces the notion that perhaps this ‘AI revolution’ is more of a stress test for human intelligence than an actual boost to productivity or code quality. Despite the authors pitching the idea that OpenAI or Google could toss a few cents the way of OSS projects when their code is being used, the comparison with Spotify is painfully apt, since about 80% of artists on Spotify rarely have their tracks played and thus receive basically no money for their efforts.

With an LLM statistical model we know with extremely high likelihood that only the dependencies that are most prevalent in the training data set will realistically be used for the output, and we expect that we’ll see something similar happen with this vibe coding compensation scheme.

Even today we can already observe many negative effects from ‘AI slop’ in software development. Whether it’ll be something that’ll choke the life out of the entire OSS ecosystem remains to be seen, but it is hard to envision a bright vibe coding future.

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