A common piece of advice for working with AI coding tools is to simply write more tests because if the tests pass, the code is fine.
大多数人认为只要测试通过,代码就是好的,但作者指出过度编辑问题使得测试难以全面评估代码质量。
A common piece of advice for working with AI coding tools is to simply write more tests because if the tests pass, the code is fine.
大多数人认为只要测试通过,代码就是好的,但作者指出过度编辑问题使得测试难以全面评估代码质量。
Claude Opus 4.7 feels like a real step up in intelligence. Code quality is noticeably improved, it's cutting out the meaningless wrapper functions and fallback scaffolding that used to pile up, and fixes its own code as it goes.
AI在代码质量和自主修复能力上的进步令人印象深刻,特别是能够消除无意义的包装函数和备用脚手架,这表明AI正在从代码生成向真正的软件开发实践转变。
Add contacts, live search, full pipeline dashboard – all unit tests passed.
令人惊讶的是:AI生成的代码不仅功能完整,包括联系人管理、实时搜索和完整的管道仪表板,而且所有单元测试都通过了,表明AI不仅能快速编码,还能保证代码质量。
their productivity is affected by the state of the codebase.
【启发】这句话的深远意义在于:它把 AI Coding Agent 与人类开发者置于同一评价维度。这不是「AI 是否能替代人」的问题,而是「AI 受代码质量影响的方式是否与人类相同」。答案是肯定的——这意味着几十年来软件工程师积累的代码质量实践,不是因为 AI 的到来而失效,而恰恰因为 AI 的到来而变得更加重要。技术债从「慢慢影响人」变成了「立刻影响 AI 的 token 消耗」。
Thinking about how you will observe whether things are working correctly or not ahead of time can also have a big impact on the quality of the code you write.
YES. This feel similar to the way that TDD can also improve the code that you write, but with a broader/more comprehensive outlook.
The DSL has a weaker control over the program’s flow — we can’t have conditions unless we add a special step
The false promise of your source code repository is that everything it contains is “good.” To complete your task, just find something that does something similar, copy, modify, and you’re done. Looking inside the same repository seems like a safety mechanism for quality but, in fact, there is no such guarantee.
What makes it good or bad is the quality of the code being multiplied.
Anyone who’s ever worked with me knows that I place a very high value on what ends up checked-in to a source code repository.