AI applications present three new disciplines to master: picking the right models, developing the hill-climbing loop, & evaluating the performance of the system for each company
作者指出AI应用开发与SaaS有本质区别，需要掌握三个新领域：选择合适模型、开发提升循环和评估系统性能。这对初学者来说是一个重要的认知转变，提醒AI应用开发需要全新的思维方式和技能集，而非传统软件开发的简单延伸。
Composer 2.5 is exceptionally intelligent & up to 10x more efficient than similarly capable models
大多数人认为开发定制AI模型需要大量资源和专业知识，但Cursor的案例表明，通过在开源模型基础上进行微调，可以实现比原始模型高10倍的效率，这一反直觉发现挑战了AI开发的资源密集型传统认知。
We must leapfrog the current paradigm. History shows us how Japan's historical dominance in manufacturing was not achieved through abundant natural resources but by fundamentally redesigning the institution of the factory floor.
大多数人认为AI发展需要大量计算资源和数据积累，但作者认为日本可以通过创新设计而非资源投入来领导AI发展，就像日本制造业的成功不是依靠自然资源而是通过重新设计工厂系统一样。这种观点挑战了当前AI行业依赖大规模计算的主流认知。
Always-available, always-agreeable companions set unrealistic expectations. AI toys never have bad days, never get tired or frustrated, never need to focus on their own needs, and never say 'not now, I'm busy.' This creates an expectation for relationships that no human can meet.
这里的发展伤害隐蔽而深远：儿童通过经验来校准自己的人际期望。一个永远在线、永远赞同的伴侣，不仅是对真实人际关系的劣质替代品，更会主动扭曲儿童对「关系应该是什么感觉」的预期基准。真实关系会因此显得令人失望或存在缺陷——不是因为它们本身如此，而是因为基线已被悄然改变。
Children age 5 and under cannot reliably distinguish AI from real people. At this developmental stage, kids are learning about relationships, trust, and how the world works. Introducing AI companions that seem to have personalities, remember conversations, and respond to emotional cues can create confusion.
这里的发展心理学特异性很重要：5岁并非随意设定的门槛。在此年龄之前，儿童处于皮亚杰的前运算阶段，尚未具备从原则上区分有生命与无生命物体的认知能力。AI玩具恰恰在大脑最容易形成「人际关系如何运作」这一基础信念的发展窗口期被引入——这一时机令问题尤为严峻。
With $500 million in funding and a reported $2.5 billion valuation, Flourish wants to reinvent AI by putting real neurons under the microscope.
大多数人认为AI发展应该依靠算法优化和计算能力提升，但作者认为Flourish通过研究真实神经元来'重新发明AI'，这是一个反主流的方法。大多数人认为AI应该模拟大脑功能，而不是直接研究大脑本身，这挑战了当前AI开发的基本共识。
Flourish wants to reinvent AI by putting real neurons under the microscope.
大多数人认为AI进步应该依靠更强大的算法和更多的数据，但这里提出了一种反直觉的方法：通过研究真实生物神经元来重新定义AI。这一观点挑战了当前AI研究的计算主义范式，暗示真正的智能可能需要生物学和计算科学的深度融合，而非单纯的数学模型。
Codex can help people take on more ambitious projects, leading to greater scope of their roles, and potentially accelerate career advancement.
大多数人认为AI会替代人类工作或限制职业发展，但作者认为AI实际上能让人承担更雄心勃勃的项目，扩大职责范围并加速职业发展。这挑战了AI导致工作减少或职业停滞的常见担忧，表明AI可能是职业扩张的催化剂而非替代品。
a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the model training pipeline.
大多数人认为模型性能的提升主要来自于算法创新和架构改进，但作者认为最大的提升往往来自于数据管道和训练管道中的小错误修复。这挑战了人们对AI模型开发过程的主流认知，暗示了工程优化可能比算法创新更重要。
Claude Code with Opus 4.8 can now carry out codebase-scale migrations across hundreds of thousands of lines of code from kickoff to merge
大多数人认为AI模型在处理大规模代码迁移时需要人工干预和审查，但作者认为Opus 4.8能够独立完成数十万行代码的全流程迁移。这挑战了软件开发领域对AI辅助能力的传统认知，暗示AI可能比人们想象的更能胜任复杂的工程任务。
Six months ago, while working on an internal productivity tool, our team made a controversial (at the time) decision: we'd build our repo with no human-written code. Every line in our project repository had to be generated by Codex.
大多数人认为软件开发必须由人类编写核心代码，但作者认为完全由AI生成代码是可行的，因为他们成功地构建了一个没有任何人工代码的仓库。这个观点挑战了软件开发的传统认知，暗示AI可能已经发展到能够独立完成整个项目的程度。
Even the ideal version, industrial megaprojects at hyperhuman scale while constantly being out over your skis with leverage sounds hellish.
大多数人认为大型AI项目和工业规模的发展是进步和繁荣的象征，但作者认为这种超人类规模的项目听起来像是地狱般的体验，因为它可能导致过度杠杆化和不可持续的压力。
We will continue improving the model's biological reasoning, expanding support for tool-heavy and long-horizon research workflows, and working closely with leading scientific institutions to evaluate real-world impact.
这一长期发展规划反映了AI科学应用的阶段性特征。从基础推理到复杂工作流程支持，再到实际影响评估，展示了AI如何逐步深入科学研究的核心，最终可能改变科学发现的本质。
As the cost of software development falls, trusted partners with broad adoption can expand faster than anyone else.
在开发成本下降的背景下，广泛采用和信任成为扩张的关键因素，这暗示AI时代的赢家可能不是技术最先进的，而是能够最快建立信任生态系统的公司。
Four researchers and software engineers estimated that a skilled human engineer would take 2 to 17 weeks to reimplement gotree, as AI successfully did in this work.
这一对比数据极具启发性，它量化了AI在特定任务上相对于人类的时间优势。这种时间压缩效应可能重塑软件开发流程，但也引发了关于AI能力与人类创造力本质差异的深层思考。
It is not common for real software to be developed the way MirrorCode tasks are structured — against a precise, programmatically checkable specification.
这一重要提醒指出了MirrorCode评估方法与实际软件开发之间的差异。虽然该基准测试提供了有价值的AI能力证据，但如何将这种能力转化为实际开发环境中的表现仍是一个开放问题，这对AI在真实世界软件工程中的应用提出了挑战。
It generated a build plan, then wrote all the code in 29 seconds.
令人惊讶的是：AI模型能在不到半分钟内完成完整的应用程序构建计划并编写全部代码，这展示了AI在软件开发领域的惊人效率，远超人类开发者的常规速度。
Agent harnesses dominate agent building and tie intimately to memory.
令人惊讶的是：代理工具(harnesses)已成为构建AI代理的主导方式，并且与记忆系统紧密相连。这表明AI代理的发展方向已经从单一功能转向了具有记忆能力的复杂系统，这种转变可能彻底改变人机交互模式。
Anthropic is expected to release Claude Opus 4.7 alongside a new AI-powered design tool for building websites and presentations
令人惊讶的是：Anthropic正在将Claude从聊天和编程工具扩展到完整的创意系统，推出能够从自然语言提示创建网站、幻灯片和完整产品的设计工具。这标志着AI竞争正从文本生成向全面的创意产品开发转变，模糊了技术与非技术用户之间的界限。
For example, developers can give an agent a controlled workspace, explicit instructions, and the tools it needs to inspect evidence:
令人惊讶的是：OpenAI的Agents SDK现在允许开发者创建一个完全受控的工作环境，让AI代理可以检查文件、运行命令和编辑代码。这种能力意味着AI系统可以更深入地与计算机系统交互，实现更复杂的任务自动化，这比大多数人想象的AI能力要强大得多。
There were 1 billion commits in 2025. Now, it's 275 million per week, on pace for 14 billion this year if growth remains linear
令人惊讶的是：软件开发提交量呈现爆炸式增长，从2025年的10亿个提交激增至每周2.75亿个，预计全年将达到140亿个。这种指数级增长反映了AI时代代码生成速度的惊人变化，远超线性预测。
Anthropic says Managed Agents is designed to cut the time it takes to move from prototype to production from months to days, with early adopters like Notion, Rakuten, Asana, Vibecode, and Sentry already using it across coding, productivity, and internal workflow automation.
令人惊讶的是：Anthropic的Claude Managed Agents将AI产品从原型到生产的时间从数月缩短到几天，这种加速不仅改变了AI开发周期，还吸引了包括Notion、Rakuten等知名企业立即采用，展示了AI基础设施服务对企业AI应用的革命性影响。
In the last year, we moved from manually editing files to working with agents that write most of our code.
令人惊讶的是：仅仅一年时间内，Cursor已经从手动编辑文件转变为让代理编写大部分代码，这展示了AI编程助手发展的惊人速度，暗示软件开发正在经历前所未有的范式转变。
McClary took the process from there, contacting the supplier himself to discuss the revised design. Within a month, the new version of the Guardian flashlight was back up for sale on Amazon and on his brand's website.
大多数人认为AI会完全取代人类在产品开发中的角色，但作者认为AI实际上增强了人类决策者的能力。Mike McClary使用AI工具缩短了产品开发周期，但仍需要亲自与供应商沟通并做出最终决策，这表明AI是辅助工具而非替代品。
The first wave of image models was mostly about making cool-looking images. This next phase is about making ordinary things look real.
大多数人认为AI图像模型的发展重点是创造越来越逼真的幻想艺术或创意内容，但作者认为下一阶段的重点是让普通日常事物看起来真实，这挑战了人们对AI图像发展方向的普遍认知。
Definitely true
just going back to the AI to the extent that the that the fourth turning meets the people who are actually doing the AI and informs the AI that actually the wheel goes this way don't listen to those guys it goes this way
for - AI - the necessity of training AI with human development - John Churchill
Hoffman, R., Mueller, S., Klein, G., & Litman, J. (2021). Measuring Trust in the XAI Context. PsyArXiv. https://doi.org/10.31234/osf.io/e3kv9
According to a 2015 report by Incapsula, 48.5% of all web traffic are by bots.
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The majority of bots are "bad bots" - scrapers that are harvesting emails and looking for content to steal, DDoS bots, hacking tools that are scanning websites for security vulnerabilities, spammers trying to sell the latest diet pill, ad bots that are clicking on your advertisements, etc.
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Content on websites such as dev.to are reposted elsewhere, word-for-word, by scrapers programmed by Black Hat SEO specialists.
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However, a new breed of scrapers exist - intelligent scrapers. They can search websites for sentences containing certain keywords, and then rewrite those sentences using "article spinning" techniques.