Technical Summary of Front‑end AI Practices: AI Quadrant Model, Hongyan AI Coder, and Nextdy Aone Copilot

This article presents a technical summary of AI applications in front‑end development. It introduces the “AI Four‑Quadrant” decision model and describes two toolchains—Hongyan AI Coder and Nextdy Aone Copilot—used to automate code generation and refactoring.

AI Four‑Quadrant Model The model plots AI investment level against integration time to help teams evaluate the feasibility of AI‑assisted tasks. Quadrant I (low AI, short time) covers simple, repetitive tasks such as UI generation and component scaffolding. Quadrant II (high AI, short time) aims to move high‑investment tasks into a one‑day delivery window. Quadrant III (high AI, longer time) focuses on complex scenarios that can be decomposed and solved by AI. Quadrant IV (low AI, long time) represents tasks not yet suitable for AI.

Hongyan AI Coder – Full‑stack Intelligent Development Platform Hongyan AI Coder supports both developers and non‑technical users. It integrates design, coding, and integration across multiple platforms (iteration management, repository creation, page module addition, VS Code Copilot assistance). In practice it improves productivity by over 30 % and can generate more than 90 % of UI code automatically.

Key capabilities include:

RAG‑enhanced generation that leverages a business‑component knowledge base to produce maintainable code.

End‑to‑end pipeline (upload → parse → generate → deploy → store) built on the Bolt architecture, linking ICE3 front‑end framework with the module platform.

WebContainer‑based runtime (WebC) that enables in‑browser compilation, preview, and terminal interaction.

Nextdy Aone Copilot – Intelligent Code Refactoring Nextdy Aone Copilot addresses large‑scale code migration. It runs as a VS Code extension, identifies refactoring intent, routes to the appropriate AI tool, and triggers CI tasks for automated code transformation and pull‑request creation.

Typical workflow:

Extension detects intent and launches the AI refactoring script.

Pre‑processing prepares prompts using the original source code.

One‑ or multi‑turn dialogue with the LLM produces the transformed code.

CI pipeline commits the changes and creates a CR.

Example React component generated by the Copilot (shown as raw code):

<div className={styles.container}>
  <div className={styles.formGroup}>
    <input type="text" className={styles.input} placeholder="请输入19位数字卡号" value={cardNo} onChange={(e) => onInputChange('number', e.target.value)} maxLength={19} />
    {cardNo?.length ? (
      <Picture source={{width: '36rpx', uri: 'https://gw.alicdn.com/imgextra/i1/O1CN01bj2paK28qGpddtbIL_!!6000000007983-2-tps-72-72.png'}} className={styles.closeIcon} onClick={() => setCardNo('')} />
    ) : null}
    <div className={styles.divider} />
  </div>
  ...
</div>

Another JSON payload used in the workflow (shown as raw JSON):

{"taskId":"79FF21E3-54A5-4EA8-B90E-7ED2A7E38DC9","pluginVersion":"4.2.8","reference":"sketch","type":"Block","id":"Block_1","__VERSION__":"2.0","props":{"style":{"width":674,"height":478},"attrs":{"x":0,"y":0}},"artboardImg":"https://img.alicdn.com/imgextra/i1/O1CN01zM4hhd1cnShMg53He_!!6000000003645-2-tps-674-478.png","children":[{...}]}

Core Architecture & Capabilities The system follows a two‑stage D2C (Design‑to‑Component) pipeline and plugin‑based interaction, enabling one‑stop development from design to deployment. It supports automatic module creation, iteration management, repository synchronization, and production‑grade code output.

Best‑Practice Cases Several real‑world projects illustrate the impact:

Qingyun project: UI code generation and skeleton‑screen animation achieved 92 % AI‑generated UI in 2 hours.

Three‑Eight Gift‑Form module: AI Coder reduced development time by 50 %.

Legacy log migration: automated conversion from arms to SLS reporting.

Rax‑Tianma module migration: ICE3 integration, CSS‑module rewriting, and component upgrades.

Accessibility adaptation: automated front‑end accessibility compliance.

Summary & Outlook Hongyan AI Coder currently automates preview and deployment, but the design‑upload step still relies on external tools (imgcook). Future work aims to integrate design‑optimization, automatic component tagging, and a fully closed‑loop “design‑to‑deployment” workflow. The platform also plans to broaden its user base to include product managers, operations, and backend teams, leveraging multimodal LLMs for document generation, prototype creation, and form‑list generation.

Team Introduction The article is authored by Mengwei, Hongyu, and Xiangbei from the Taobao Self‑operated Technology Team, which supports the entire Taobao Self‑operated ecosystem.