[{"data":1,"prerenderedAt":45},["Reactive",2],{"news-story-news-20261025-x-discussion-arpit-bhayani-no-human-codes-better-than-llms":3},{"id":4,"title_zh":5,"title_en":6,"summary_zh":7,"summary_en":8,"takeaways_zh":9,"takeaways_en":15,"content_zh":21,"content_en":22,"external_links":23,"source":24,"category":31,"impact_score":32,"views_count":33,"likes_count":34,"related_entities":35,"tags":39,"image_url":44},"news-20261025-x-discussion-arpit-bhayani-no-human-codes-better-than-llms","X 热议：工程师 Arpit Bhayani 称「没有人类能比 LLM 写出更好的代码」，7 千多赞、240 多条回复引发 AI 编程能力之争","X discussion: engineer Arpit Bhayani says \"no human will ever write code better than LLMs\", drawing 7K+ likes, 240+ replies and a debate over AI coding ability","工程师 Arpit Bhayani（@arpit_bhayani）在 UTC 2026-10-25 13:37 发帖称「No human will ever write code better than LLMs. Accept this fact, stop pretending, and move on.」（[原帖](https://x.com/arpit_bhayani/status/2114350692551082390)）。抓取时约 7,193 赞、496 转发、104 引用、241 回复、529 收藏、17.4 万浏览，并进入 X 热门话题。回复两极：有人认同 LLM 写代码又快又好，也有人反驳说 LLM 常写出超出需要的冗长代码，生产环境的边界情况、架构和最终验证仍要靠人。这是一条 X 热议，观点按观点呈现，不是已证实的结论。","At 13:37 UTC on 2026-10-25, engineer Arpit Bhayani (@arpit_bhayani) posted: \"No human will ever write code better than LLMs. Accept this fact, stop pretending, and move on.\" ([post](https://x.com/arpit_bhayani/status/2114350692551082390)). When we pulled it, the post had about 7,193 likes, 496 reposts, 104 quotes, 241 replies, 529 bookmarks and 174K views, and it reached X's trending stories. Replies are split: some agree LLMs already write code faster and better, while others push back that LLMs often produce more code than a task needs and that production edge cases, architecture and final verification still depend on people. This is an X discussion presented as opinions, not a proven conclusion.",[10,11,12,13,14],"主帖：[Arpit Bhayani 原帖](https://x.com/arpit_bhayani/status/2114350692551082390)，UTC 10-25 13:37 发布，抓取时约 7,193 赞、496 转发、104 引用、241 回复、529 收藏、17.4 万浏览","原话是「没有人类能比 LLM 写出更好的代码，接受这个事实，别再假装了，往前走」，是个人观点，没有附数据或实验","支持方：有回复者称自己写了多年代码，LLM 写得比自己更好更快","反对方：有回复者指出 LLM 往往生成比需求多得多的代码，浏览器兼容、无障碍和生产环境边界情况仍要人来判断","共识方向：多条回复认为人的价值在设计、监督和验证；网上流传的裁员和招聘比例数字本文未核实，不采用",[16,17,18,19,20],"Main post: [Arpit Bhayani's post](https://x.com/arpit_bhayani/status/2114350692551082390), published 13:37 UTC on 10-25, with about 7,193 likes, 496 reposts, 104 quotes, 241 replies, 529 bookmarks and 174K views when we pulled it","The wording is \"No human will ever write code better than LLMs. Accept this fact, stop pretending, and move on\"; it is a personal opinion with no data or experiment attached","For: one reply says the author coded for years and LLMs now code better and faster than they do","Against: other replies say LLMs tend to generate far more code than a simple use case needs, and that browser quirks, accessibility and production edge cases still need human judgment","Common ground: many replies place human value in design, supervision and verification; layoff and hiring-ratio figures circulating online were not verified here, so we do not use them","### 核心背景与行业痛点\n过去几个月，Claude Opus 5.5 等模型在 agentic coding 上的表现让「AI 会不会取代程序员」的讨论再次升温。UTC 10-25 13:37，以系统设计和数据库内容著称的工程师 Arpit Bhayani（@arpit_bhayani）发了一句很绝对的话：「No human will ever write code better than LLMs. Accept this fact, stop pretending, and move on.」（[原帖](https://x.com/arpit_bhayani/status/2114350692551082390)）。这条帖子迅速进入 X 热门话题。\n\n### 架构亮点与底层机制\n这条帖子本身只是一句观点，没有附带实验、跑分或数据。讨论的焦点在于「写代码」的定义：一方把它理解为生成正确、可运行代码的速度和质量；另一方认为写代码还包括理解需求、做架构取舍、处理连作者自己都没意识到的依赖和边界情况。有回复者指出，代码是人先在脑中设计好，LLM 只是把口头设计转成代码；也有人认为 LLM 生成的代码往往比需求更冗长，不够简洁。\n\n### 权威 Benchmark 与实测跑分对比\n本条是 X 热议，不涉及跑分。可核对的是互动数据：抓取时原帖约 7,193 赞、496 转发、104 引用、241 回复、529 收藏、17.4 万浏览。网上相关讨论中流传的企业裁员案例、AI 工时和初级岗位招聘比例等数字，本文没有找到可核实的一手来源，因此不采用。\n\n### 开发者实战落地与开箱指南\n不管站哪一边，回复里比较一致的看法是：开发者的价值正在从「亲手敲代码」转向设计、审查和验证。实际做法上，可以让 AI 负责初稿和重复劳动，自己把精力放在需求澄清、架构边界、测试和代码评审上，并警惕 AI 生成过多不必要的代码。想看各方观点，可以直接打开原帖的回复和引用。","### Background and the problem\nOver the past few months, the agentic coding results of models such as Claude Opus 5.5 have reignited the question of whether AI will replace programmers. At 13:37 UTC on 10-25, Arpit Bhayani (@arpit_bhayani), an engineer known for system design and database content, posted a blunt line: \"No human will ever write code better than LLMs. Accept this fact, stop pretending, and move on.\" ([post](https://x.com/arpit_bhayani/status/2114350692551082390)). It quickly reached X's trending stories.\n\n### How it works\nThe post is a single opinion with no experiment, benchmark or data attached. The debate is mostly about what \"writing code\" means: one side treats it as the speed and quality of producing correct, runnable code; the other says it also covers understanding requirements, making architecture trade-offs, and handling dependencies and edge cases the author may not even be aware of. One reply argues that the human designs the code first and the LLM only turns that spoken design into code; another says LLM output is often more verbose than the task needs.\n\n### Benchmarks and measured results\nThis is an X discussion with no benchmark involved. The numbers we can check are engagement: when we pulled it, the post had about 7,193 likes, 496 reposts, 104 quotes, 241 replies, 529 bookmarks and 174K views. Company layoff stories, AI work-hour figures and junior hiring ratios circulating in related discussion had no verifiable first-hand source we could find, so we do not use them.\n\n### What developers can do now\nWhichever side people take, a common view in the replies is that developer value is shifting from typing code by hand toward design, review and verification. In practice, let AI handle first drafts and repetitive work, spend your own effort on clarifying requirements, architecture boundaries, tests and code review, and watch for AI adding code you do not need. To see the range of views, open the replies and quotes on the original post.",[],{"platform":25,"author_name":26,"author_handle":27,"author_avatar":28,"tweet_url":29,"published_at":30},"x","Arpit Bhayani","@arpit_bhayani","https://unavatar.io/x/arpit_bhayani","https://x.com/arpit_bhayani/status/2114350692551082390","2026-10-25T13:37:42.000Z","tooling",81,34,0,{"models":36,"agents":37,"vendors":38},[],[],[],[40,41,42,43,26],"X热议","AI编程","LLM","程序员","",1793718872598]