2026 Domestic AI Coding Tools Evaluation: Building an E-commerce Site from Scratch

Introduction: AI Coding Tools Enter the Practical Phase

The AI coding assistant landscape in 2026 is vastly different from two years ago. With the expansion of LLM context windows and the maturity of Agent scheduling capabilities, this generation of tools is no longer satisfied with simple “auto-completion.” Instead, they are tackling the ultimate challenge: building complete projects from scratch. For developers utilizing domestic tools, selecting a solution that offers fast response times, seamless local network access, and a deep understanding of regional development habits has become key to improving efficiency.

This article skips theoretical discussions and dives directly into hands-on testing. We selected a typical and highly representative task: building an e-commerce frontend site from scratch using React, comprising 10 core pages. This task covers almost all daily frontend development scenarios, including list rendering, routing, state management, form validation, and nested layouts for user centers. The core challenge lies in maintaining “site completeness and overall consistency.”

We will deeply evaluate three of the most representative domestic AI coding tools across five dimensions: UI friendliness, task execution mode, output quality, code structure, and deployment smoothness. All conclusions are based on actual iteration processes rather than vendor marketing parameters.

Note: To avoid commercial promotion, the specific products are referred to as “Tool A,” “Tool B,” and “Tool C.” However, all evaluation details and real-world experiences are fully preserved.

1. Evaluation Tools and Environment Setup

1.1 Overview of Participating Tools (Domestic server connectivity, no network proxy required)

Code	Core Features	Target Audience
Tool A	High UI affinity, features a “one-click” task flow mode	Developers seeking rapid prototyping and valuing IDE aesthetics
Tool B	Mature interaction logic, supports multi-Agent parallel decision-making	Power users familiar with C-style interactions and willing to engage in multi-round debugging
Tool C	Deeply integrated with backend cloud resources, suited for full-stack scenarios	Teams requiring direct linkage with cloud functions and databases

1.2 Task Definition and Acceptance Criteria

• Prompt: Build a frontend shopping site using React. It must include: Homepage (product list), Product Detail page, Checkout page, Payment Success page, Login page, Register page, User Center - Dashboard, User Center - Orders, User Center - Wallet (Coupons), and User Center - Settings.
• Implicit Requirements: Closed-loop navigation logic between pages, basic required field validation for forms, and a unified overall visual style.
• Environment: Node.js 20.x, default scaffolding (Vite), zero manual intervention.

2. Hands-On Combat: Real Performance of the Three Tools

2.1 Tool A: Excellent “One-Shot” Experience, but Obvious Shortcomings

UI and Interaction: Tool A’s UI design is the most “appealing” among its peers. It abandons the cold dark themes and cluttered sidebars of traditional IDEs in favor of softer colors and a card-based layout. Its unique “streaming build” mode is distinctive—unlike the standard “fully automated” approach, it tends to highlight each key action step, allowing developers to intervene in the process without breaking the rhythm. Throughout the task execution, the server response was rapid with no lag, which is rare among domestic tools.

Output Analysis:
The final generated site performed acceptably in terms of functional closure. Clicking a product on the homepage smoothly redirects to the details page, and adding it to the cart allows for a simulated payment process leading to the success page. The forms feature basic “non-empty” validation, and the login/registration cross-navigation logic is correct. However, the output has three significant issues:

1. Overall style leans towards mobile: Although simple responsive design was implemented, the layout feels noticeably stretched on desktop browsers. Elements on the login and registration pages specifically suffer from overflow and deformation.
2. Lack of content depth: The product detail page contains too little information, only featuring a placeholder image and price text, lacking core modules like SKU selection and reviews.
3. Strong coupling between data layer and UI: Mock data is hardcoded directly inside the components, making it inconvenient to switch to an API request mode. This is not friendly for subsequent engineering iterations.

Code Architecture Review: The code file structure is clear, strictly separating components and pages directories, with independent style file management. However, the project hierarchy is too flat. There is no independent router configuration or api request encapsulation layer, resulting in poor architectural scalability.

2.2 Tool B: Powerful Interaction Control, but Low Initial Fault Tolerance

UI and Interaction: Tool B’s operational logic clearly inherits the interaction paradigm of mainstream international AI IDEs, complete with shortcuts, cursor tracking, and Diff views. Its hallmark “multi-Agent parallel mode” is a major highlight—it simultaneously dispatches several Agents to handle page routing, component writing, and style management separately, finally merging the results through a “review queue.” However, the problem lies here: when the initial generation task is rolled out all at once, the excessive number of pending items increases cognitive load, causing users to hesitate between “merging requirements” and “regenerating.”

Output Analysis and Debug Process:
Tool B’s initial generation result was not perfect and had quite a few issues. Although the build process succeeded, the browser console immediately threw route mismatch errors when the project ran. We had to conduct multiple rounds of manual conversational corrections (about 5-6 times) to get the core workflow running. Nevertheless, Tool B showed highlights in detail handling:

• The form validation logic was quite comprehensive, including not only non-empty checks but also email format validation and password strength prompts.
• The information density on the product detail page was significantly better than Tool A’s, including specification parameters and a carousel structure.

However, it made major mistakes in styling: the default button color was white text on a white background, making it indistinguishable against certain page backgrounds. Furthermore, it completely lacked mobile adaptation, resulting in an obtrusive horizontal scrollbar on narrow screens. The visual color scheme lacked an overall tone, and variable conflicts between some component libraries and custom styles made the pages look cobbled together.

2.3 Tool C (Supplementary Dimension): New Variables Brought by Cloud Integration

During actual testing, we also briefly experienced another tool, Tool C, which focuses on cloud full-stack capabilities. Its characteristic is that it is no longer limited to generating frontend code; it can directly create cloud database table structures and function entry points within the same conversation flow. This means that when generating the “Orders” page, the tool conveniently produces the backend API call code and mock authentication logic. Although this task did not require a backend, the Mock data interface it generated was dynamically intercepted, providing huge convenience for subsequent integration. The downside is that its frontend layout generation capability is poor, the interface looks rough, and the entire generation process takes a long time.

3. Deep Insights: Why Is There Still a Gap Between “Can Generate” and “Usable”?

Through this hands-on test, we can extract the commonalities and differences of domestic AI coding tools in 2026:

1. The Route Dispute of Generation Modes: Tool A chooses a “smooth flow” execution to minimize interruptions, suitable for rapid prototyping. Tool B chooses “multi-Agent parallelism,” emphasizing controllability but easily leading to choice paralysis. Essentially, this is a trade-off between “pursuing single-pass success rate” and “pursuing process intervenability.”
2. The Plateau of Code Quality: The code generated by the vast majority of tools no longer has major issues at the logical and syntactic level. The primary points of failure are concentrated in engineering structure layering and style fidelity. Evolving from “writing runnable code” to “writing maintainable architecture” is currently a major training difficulty for large models.
3. The Scarcity of Aesthetics: This is a common flaw across all current tools. When the prompt does not provide a clear design system (such as color palettes or font specifications), the model tends to use random but “safe” defaults. The result is “usable but ugly,” and style drifting often occurs between different pages.
4. Iteration Cost and Context Loss: As project files increase, the tool’s context understanding ability drops significantly. It might accidentally break the routing of page B while modifying the styles of page A. Optimizing the attention mechanism for long conversations is a key breakthrough point for the next stage.

4. Selection Recommendations and Practical Experience Summary

Based on the above hands-on testing, we provide the following selection references (specifically for domestic direct-connection scenarios):

• If you are a product manager or an early-stage startup team needing to quickly validate a closed-loop business logic, Tool A is more suitable. Its auto-completion level and interface comfort can significantly reduce communication costs.
• If you are an experienced mature developer, and your frontend code mostly requires secondary refactoring, Tool B‘s multi-Agent collaboration and stronger form logic generation capabilities can serve as a “copilot” to standardize your coding habits.
• If you are responsible for full-stack development, and the project heavily depends on backend resources, Tool C‘s cloud resource linkage feature is worth tracking closely, despite its current relative weakness in frontend visuals.

Additional Tips for Developers:

• Prompt engineering is crucial: Do not just throw a single sentence in the first conversation. It is recommended to include strong constraint information in the prompt, such as tech stack versions, UI style tendencies (e.g., “refer to Ant Design default styles”), and whether responsive adaptation is required.
• Establish a “human fallback” mechanism: No matter how powerful the tool is, please maintain the habit of Code Review for core code, especially for modules involving payment processes or user privacy.

Conclusion: The Second Half of AI Coding is an “Engineering” Competition

In 2026, domestic AI coding tools have crossed the passing line of “can it write code” and are exploring the deep waters of “does it write well.” Today’s hands-on test clearly shows: tool competition has shifted from model parameter scale to a comprehensive understanding of real engineering structures, UI design aesthetics, and interaction paths. For developers, this also means a new era—our core value is no longer typing out every line of code, but learning how to more precisely “command” these tools, and possessing keen aesthetic and architectural judgment to correct their shortcomings.

The future IDE will no longer be just an editor, but a “conversational R&D partner” that integrates generation, verification, and deployment. Who can walk more steadily on this path will be decided by time.