RunByAI

Tag: AI programming

32 post(s)

October 2, 20260 comment(s)

Context Engineering: A Lesson More Important than Hint Word Engineering in the Age of AI Agents

In the past two years, 'Prompt Engineering' has been an essential introductory course for almost all AI application developers. But as the large model enters the production environment, more and more

Tech View AI Agent AI programming

September 16, 20260 comment(s)

AI Programming Agent: From "Completing a Line" to "Taking Over a Task"

In the past few years, the process of AI coding has undergone three significant morphological changes: from "guessing the next line you want to input" in the IDE, to "you said I changed a piece of cod

Tech Guide AI programming Copilot

September 12, 20260 comment(s)

The Second Half of AI Coding: From Autocomplete to Autonomous Bug Fixing

过去两年，AI 编程工具的主战场是“补全”：在编辑器里预测下一行、生成函数骨架、根据注释写出实现。这类能力的价值已经被广泛验证，但它解决的仍然是人明确知道“要写什么”的场景。真正耗时的部分，往往不是写新代码，而是理解既有代码、定位缺陷、验证修复——而这正是 AI 编程正在迈入的下半场。一、从“生成”到“修改”的范式转变补全类工具的输入是意图，输出是新增代码；而缺陷修复类任务的输入是一段行为异常的现

Tech View AI programming

September 11, 20260 comment(s)

Context engineering of AI programming assistant: making models truly understand your codebase

More and more teams have integrated AI programming assistants into daily development, but with the same tools, the quality of output varies greatly. The reason often lies not in the strength of the mo

Tech AI programming

September 9, 20260 comment(s)

Refactoring Legacy Systems with AI Coding Assistants: A Pragmatic Roadmap

遗留系统的重构常常让人望而却步：代码缺乏测试、文档过时、业务规则藏在深层函数里。AI 编程助手的出现，让这件事有了更平滑的切入方式，但前提是把它当作「结对程序员」，而不是「一键重写器」。第一步是让 AI 先读懂现状，再谈改造。把模块的入口与核心函数交给助手，请它用自然语言概括职责、标注可疑点，并整理出调用关系。这个过程看似简单，却能显著降低上手成本——人负责判断方向，AI 负责梳理细节。第二步是「

Tech Guide AI programming

September 7, 20260 comment(s)

Keeping AI code from crashing: Three prerequisite agreements for requirements, acceptance, and regression

Many people have had the experience of using AI programming assistants: making small changes is easy, but once it involves modifying logic or structure, the code provided by AI often looks right but r

Tech Guide AI programming

September 7, 20260 comment(s)

Advanced Tip: Five Practical Methods for Stable Output of Structured Results from Large Models

The most headache inducing problem when integrating a large model into a real business system is often not "the model doesn't understand", but "the model output is uncontrollable" - the same question,

Tech Guide AI programming

September 6, 20260 comment(s)

Using AI Coding Assistants Efficiently: A Three-Step Loop of Decomposition, Testing, and Human Review

Many developers swing between seeing AI coding assistants as all-powerful or hopelessly unreliable. In practice these tools excel at small, well-specified tasks and struggle with vague, complex ones.

Tech Guide AI programming

September 4, 20260 comment(s)

Zero Foundation AI Programming: A Four Step Approach to Turning Ideas into Runable Code

Many non programmers want to use AI to write some small tools: batch renaming files, organizing tables, crawling webpage titles... There are many ideas, but they don't know where to start. In fact, fo

Tech Guide AI programming

September 3, 20260 comment(s)

Quality Assurance for AI-Generated Code: Testing, Review, and Human Oversight in Engineering Practice

AI 编程助手已经从「补全几行代码」进化到「理解整个代码仓库、跨文件修改」，越来越多开发者把日常编码任务交给 AI。但一个现实问题随之而来：AI 生成代码的质量由谁来保证？如果只是「跑得通就复制粘贴」，隐患会悄悄沉淀进代码库。AI 生成代码的典型风险首先是正确性风险。大模型基于概率生成代码，看起来合理不代表逻辑正确，边界条件、并发问题、异常处理常常是重灾区。其次是安全风险：AI 可能写出存在漏洞的

Tech Guide AI programming

32 post(s)