我的征尘是星辰大海。。。
The dirt and dust from my pilgrimage forms oceans of stars...
-------当记忆的篇章变得零碎，当追忆的图片变得模糊，我们只能求助于数字存储的永恒的回忆
作者:黄教授
手机视频列表
观国产零代码软件出圈有感
视频
音频
原始脚本
观国产零代码软件出圈有感，今日一篇关于九十兆国产零代码软件的文章引发热议。 文中盛赞这款拖拽式工具的能力。 业务人员无需掌握编程知识，简单拖拽组合，短短数日就能搭建出专属业务系统。 优秀开发者还能拿到企业现金奖励。 一时间，这款零代码工具仿佛成了降本增效的行业新解法。 通读内容不难发现，这篇文章带有浓重的产品宣传色彩。 刻意放大了传统拖拽零代码的价值，却忽略了人工智能浪潮下，软件开发行业正在发生的颠覆性变革。 在过往的软件开发模式里。 企业搭建一套贴合自身流程的业务系统，始终面临着极高的门槛。 专业程序员开发周期长，人力成本高昂，业务部门的细碎需求还常常出现沟通偏差。 而拖拽式零代码平台的出现，恰好补齐了这一短板，把编程门槛大幅降低，让业务人员能亲手搭建适配自身工作的系统。 低门槛快落地，也成了这类软件最核心的竞争优势。 可如今 AI 口述编程，也就是 Web Coding 的到来，彻底撼动了传统零代码的立身之本。 以往需要手动拖拽组件、梳理流程、配置规则才能完成的开发工作，现在业务人员只用自然语言口述需求，大模型便能一键生成系统雏形。 对比繁琐的拖拽操作，自然语言表达更贴合人的思维习惯，开发效率实现了量级提升。 零代码引以为傲的低门槛优势瞬间被 AI 抹平。 或许有人会提出质疑，AI 生成的代码和系统存在幻觉问题，逻辑难免出现疏漏，可靠性远不如人工开发、拖拽搭建的系统。 这个观点确实客观中肯，AI 现阶段依旧存在稳定性短板。 但我们不妨跳出固有思维，看清软件开发里一条天然规律。 系统开发难度极高，落地验证却相对简单。 搭建一套包含流程审批、数据联动、权限管理的企业级业务系统，需要梳理繁杂的业务逻辑。 耗费大量时间打磨细节。 但系统成型之后，核验工作却轻松许多。 我们可以用 Excel 对账，核验数据准确性，用人工复盘排查流程漏洞。 也可以依托专业校验平台完成合规检测，甚至沿用企业原本的质检复合流程就足够完成验证。 更关键的是，验证本就是企业级应用上线前的必经环节。 无论系统由程序员手写开发、业务人员拖拽搭建，还是 AI 口述生成，核验的成本与流程都不会凭空增加。 原本卡在企业数字化转型路上的最大瓶颈。 是漫长且高成本的开发环节，而 AI 恰好精准攻克了这一痛点。 我们完全可以形成全新的开发模式。 AI 负责高效完成0~1的系统搭建，原有零代码平台承接逻辑固化、合规管控与长期运维，再搭配人工或工具完成精准核验。 兼顾效率、成本与稳定性。 资本市场看空纯拖拽类零代码企业，背后的逻辑也正在于此。 这类产品没有构筑深厚的技术壁垒。 核心能力只是简化开发操作，而这恰恰是大模型最擅长的领域。 当 AI 能以更低门槛、更快速度完成系统开发，仅靠拖拽操作立身的零代码软件。 注定会失去市场竞争力。 放眼整个软件开发行业，变革的趋势已然清晰。 传统手写代码、纯拖拽、零代码都不再是单一最优解。 AI 生成开发加零代码托管运维加标准化落地核验才是未来数字化开发的主流模式。 那篇大肆吹捧老式零代码工具的推文。 不过是旧时代产品的最后营销包装。 时代的浪潮永远向前，AI 正在重构软件开发的全链路，不肯拥抱 AI 固守拖拽单一模式的产品。 终究会被行业慢慢淘汰。
修正脚本
观国产零代码软件出圈有感，今日一篇关于某一款国产零代码软件的文章引发热议。 文中盛赞这款拖拽式工具的能力。 业务人员无需掌握编程知识，简单拖拽组合，短短数日就能搭建出专属业务系统。 优秀开发者还能拿到企业现金奖励。 一时间，这款零代码工具仿佛成了降本增效的行业新解法。 通读内容不难发现，这篇文章带有浓重的产品宣传色彩。 刻意放大了传统拖拽零代码的价值，却忽略了人工智能浪潮下，软件开发行业正在发生的颠覆性变革。 在过往的软件开发模式里。 企业搭建一套贴合自身流程的业务系统，始终面临着极高的门槛。 专业程序员开发周期长，人力成本高昂，业务部门的细碎需求还常常出现沟通偏差。 而拖拽式零代码平台的出现，恰好补齐了这一短板，把编程门槛大幅降低，让业务人员能亲手搭建适配自身工作的系统。 低门槛快落地，也成了这类软件最核心的竞争优势。 可如今 AI 口述编程，也就是 Web Coding 的到来，彻底撼动了传统零代码的立身之本。 以往需要手动拖拽组件、梳理流程、配置规则才能完成的开发工作，现在业务人员只用自然语言口述需求，大模型便能一键生成系统雏形。 对比繁琐的拖拽操作，自然语言表达更贴合人的思维习惯，开发效率实现了量级提升。 零代码引以为傲的低门槛优势瞬间被 AI 抹平。 或许有人会提出质疑，AI 生成的代码和系统存在幻觉问题，逻辑难免出现疏漏，可靠性远不如人工开发、拖拽搭建的系统。 这个观点确实客观中肯，AI 现阶段依旧存在稳定性短板。 但我们不妨跳出固有思维，看清软件开发里一条天然规律。 系统开发难度极高，落地验证却相对简单。 搭建一套包含流程审批、数据联动、权限管理的企业级业务系统，需要梳理繁杂的业务逻辑。 耗费大量时间打磨细节。 但系统成型之后，核验工作却轻松许多。 我们可以用 Excel 对账，核验数据准确性，用人工复盘排查流程漏洞。 也可以依托专业校验平台完成合规检测，甚至沿用企业原本的质检复核流程就足够完成验证。 更关键的是，验证本就是企业级应用上线前的必经环节。 无论系统由程序员手写开发、业务人员拖拽搭建，还是 AI 口述生成，核验的成本与流程都不会凭空增加。 原本卡在企业数字化转型路上的最大瓶颈。 是漫长且高成本的开发环节，而 AI 恰好精准攻克了这一痛点。 我们完全可以形成全新的开发模式。 AI 负责高效完成0~1的系统搭建，原有零代码平台承接逻辑固化、合规管控与长期运维，再搭配人工或工具完成精准核验。 兼顾效率、成本与稳定性。 资本市场看空纯拖拽类零代码企业，背后的逻辑也正在于此。 这类产品没有构筑深厚的技术壁垒。 核心能力只是简化开发操作，而这恰恰是大模型最擅长的领域。 当 AI 能以更低门槛、更快速度完成系统开发，仅靠拖拽操作立身的零代码软件。 注定会失去市场竞争力。 放眼整个软件开发行业，变革的趋势已然清晰。 传统手写代码、纯拖拽、零代码都不再是单一最优解。 AI 生成开发加零代码托管运维加标准化落地核验才是未来数字化开发的主流模式。 那篇大肆吹捧老式零代码工具的推文。 不过是旧时代产品的最后营销包装。 时代的浪潮永远向前，AI 正在重构软件开发的全链路，不肯拥抱 AI 固守拖拽单一模式的产品。 终究会被行业慢慢淘汰。
英文翻译
Reflections on the Rise of Domestic No-Code Software: Today, an article about a domestic no-code software sparked heated discussion. The article praised the capabilities of this drag-and-drop tool. Business personnel, without needing programming knowledge, can simply drag and drop components to build a customized business system in just a few days. Outstanding developers can even receive cash rewards from the enterprise. For a moment, this no-code tool seemed to become the new industry solution for cost reduction and efficiency improvement. Reading through the content, it is not hard to see that this article carries a strong product promotion tone. It deliberately exaggerates the value of traditional drag-and-drop no-code, while ignoring the disruptive changes taking place in the software development industry under the wave of artificial intelligence. In the traditional software development model, building a business system that fits an enterprise's own processes has always faced extremely high barriers. Professional programmers require long development cycles, high labor costs, and the fragmented needs of business departments often suffer from communication deviations. The emergence of drag-and-drop no-code platforms has exactly filled this gap, significantly lowering the programming threshold and allowing business personnel to build systems that suit their own work. Low barriers and fast implementation have become the core competitive advantages of such software. But now, the arrival of AI oral programming—Web Coding—has completely shaken the foundation of traditional no-code. Development work that previously required manually dragging components, sorting out processes, and configuring rules can now be accomplished by business personnel simply describing their needs in natural language, with the large model generating a system prototype in one click. Compared to the tedious drag-and-drop operations, natural language expression is more aligned with human thinking habits, achieving an order-of-magnitude improvement in development efficiency. The low-barrier advantage that no-code prided itself on has been instantly leveled by AI. Some may question that AI-generated code and systems suffer from hallucination issues, with inevitable logic flaws, and their reliability is far inferior to systems developed manually or built by drag-and-drop. This view is indeed objective and fair—AI still has stability shortcomings at this stage. But let's step out of fixed thinking and recognize a natural law in software development: system development is extremely difficult, but verification after implementation is relatively simple. Building an enterprise-level business system that includes process approval, data linkage, and permission management requires sorting out complex business logic and spending a lot of time refining details. However, once the system is formed, verification work becomes much easier. We can use Excel for reconciliation to verify data accuracy, manually review and troubleshoot process loopholes, rely on professional verification platforms for compliance checks, or even follow the enterprise's original quality inspection and review process to complete verification. More importantly, verification is a necessary step before any enterprise-level application goes live. Whether the system is hand-coded by programmers, built by business personnel through drag-and-drop, or generated by AI, the cost and process of verification will not increase out of thin air. The biggest bottleneck that originally blocked enterprises on their digital transformation journey was the lengthy and high-cost development phase, and AI happens to precisely solve this pain point. We can form a completely new development model: AI is responsible for efficiently completing system construction from 0 to 1, the existing no-code platform takes over logic solidification, compliance control, and long-term operations, and then manual or tool-based precise verification is added. This balances efficiency, cost, and stability. Capital markets are shorting pure drag-and-drop no-code companies, and the logic behind this is exactly the same. Such products have not built deep technical barriers; their core capability is merely simplifying development operations—which happens to be the field where large models excel. When AI can complete system development at a lower threshold and faster speed, no-code software that relies solely on drag-and-drop operations is destined to lose market competitiveness. Looking at the entire software development industry, the trend of change is already clear: traditional hand-coding, pure drag-and-drop, and no-code are no longer the single optimal solutions. AI-generated development plus no-code hosted operations plus standardized verification is the mainstream model for future digital development. The post that heavily praised the old no-code tool is nothing more than the final marketing packaging of a bygone era's product. The tide of the times always moves forward. AI is reconstructing the full chain of software development. Products that refuse to embrace AI and cling to the single drag-and-drop model will eventually be slowly phased out by the industry.
back to top