🛒 An AI-assisted full-process development solution for mini program development teams and independent developers, covering the entire process of UI development, logic development, cloud development, multi-terminal adaptation, and review and launch of multi-platform mini programs such as WeChat/Alipay/Douyin, etc., and adapting to cross-terminal frameworks such as Taro/uni-app, using AI to reduce duplication of labor and improve delivery quality.

AI assisted small program development solution

1. Plan Overview

Track background

Mini programs are a unique super application ecosystem in China. Multiple platforms such as WeChat Mini Program, Alipay Mini Program, Douyin Mini Program, Baidu Mini Program, and QQ Mini Program coexist, covering almost all online service scenarios such as social networking, e-commerce, travel, government affairs, education, and games. According to public data from WeChat, the number of daily active users of WeChat mini programs has exceeded 600 million, and the number of mini program developers exceeds 10 million.

The development model of small programs is significantly different from traditional mobile development:

• Multi-platform parallel: The same business needs to adapt to multiple platforms such as WeChat/Alipay/Douyin, etc., and code reuse and differentiation coexist.
• Proprietary syntax: WeChat uses WXML+WXSS+JS/TS, Alipay uses AXML+ACSS, and different platform components and APIs are inconsistent
• Package size limit: The main package of the WeChat applet does not exceed 2MB, and the sub-package does not exceed 8MB. It is sensitive to code size.
• Cloud development integration: WeChat cloud development provides cloud functions/databases/storage to shorten the back-end development path
• Audit cycle: Each platform has different audit rules, and the listing schedule affects the business rhythm.

The value of AI-assisted mini program development lies in handing over repetitive UI development, template code writing, multi-terminal code conversion, document review and other tasks to AI, allowing developers to focus on business logic, architecture design and user experience optimization.

User portrait

ROI estimation

Preconditions

• Have basic programming skills in JavaScript/TypeScript
• Have a computer that can run WeChat developer tools and other IDEs smoothly (16GB+ memory recommended)
• Registered WeChat Mini Program/Alipay Mini Program and other platform developer accounts
• Access to mainstream AI tool platforms

2. Tool chain list

Note: Platform tools such as WeChat Developer Tools and Alipay IDE are essential local IDEs and have no substitutes. The stacked use of AI programming tools can cover the entire process, and beginners can start with a single tool.

3. Preparation (Checklist)

Before starting development, please confirm the following items one by one:

• [ ] Register a mini program platform account: WeChat mini program developer account (requires enterprise or individual certification, ￥30 certification fee), Alipay mini program account, Douyin mini program account, etc.
• [ ] Install development environment: WeChat Developer Tools (stable version), VS Code (or Cursor), Node.js 18+
• [ ] AI tool account: Register Cursor (or install VS Code + GitHub Copilot plug-in), Claude account
• [ ] Code management configuration: Create a Git repository (GitHub/GitLab) and configure .gitignore (including node_modules, miniprogram_npm)
• [ ] Cloud development configuration (if required): Activate the WeChat cloud development environment and obtain the environment ID
• [ ] Configuration Error Monitoring: Create Sentry project, obtain DSN and integrate into Mini Program SDK
• [ ] Design draft and requirement documents: Prepare prototype drawings (Figma/Ink Knife), interaction instructions, and API interface documents
• [ ] Technical Solution Selection: Decide whether to use a cross-end framework (Taro/uni-app/kbone), confirm the UI component library (WeUI/Vant Weapp/Ant Design Mini)
• [ ] Project management settings: Build a task dashboard and define milestones in GitHub Projects / Notion / Feishu
• [ ] Local test mobile phone: Prepare at least 2 real mobile phones (iOS + Android) for real device debugging

4. Step-by-step implementation guide

Step 1: Project initialization and engineering construction

⏱ Estimated time: 1-2 days 🎯 Goal: Create a standardized mini program project skeleton, configure scaffolding, dependency management and construction process ⚠️ Prerequisites: The development environment is ready, and the technical solution selection is confirmed

Operation instructions

Project initialization determines the underlying structure for all subsequent development. Correct scaffolding configuration can save a lot of project configuration time.

Specific operations

1. Choose a scaffolding solution

• Native WeChat Mini Program: Create projects directly using WeChat developer tools, suitable for simple single-platform applications
  • Taro: npx @tarojs/cli init my-app, write and compile to multiple terminals once
  • uni-app: npx @dcloudio/uni-preset-vue#vue3 create my-app, covering WeChat/Alipay/Douyin
  • Initialize using Cursor Agent:

    Open a terminal in Cursor and switch to the target directory,
    In Agent mode, enter: "Use Taro 3.x to initialize a WeChat applet project and use TypeScript template.
    Install Vant Weapp as UI component library, configure ESLint and Prettier"

2. Configure project structure

   Use Cursor or GitHub Copilot to generate a standard directory template:

   #Ask AI: Generate project directory specifications for Taro applet
   #Expected output directory structure:
   src/
    pages/ # Page directory
    components/ # Public components
    utils/ # Utility function
    services/ # API service layer
    store/ #State management
    assets/ # Static resources

3. Configure cloud development environment (if required)

   • Enable cloud development in WeChat developer tools and create two official/test environments
   • Use Claude to generate a cloud function template: Ask "to generate a WeChat cloud function template, including input parameter verification, error handling and log output"
   • Configure the cloud environment initialization code in app.js

4. Integrated Sentry error monitoring

   Integrating Sentry into the WeChat applet requires referring to the special restrictions of the WeChat applet (no window/document objects).

   ChatGPT Operation example: "Please provide the integration code of Sentry in the WeChat applet, using the @sentry/wx-miniapp package or manual integration, including DSN configuration and global error capture"

5. Configure CI/CD (optional advanced)

• Configure GitHub Actions to automate builds
  • Use WeChat Developer Tools CLI to implement automated uploads

Verification method

• [ ] npm run dev:weapp can be compiled normally and previewed in WeChat developer tools
• [ ] Basic page (index) opens without error
• [ ] Cloud development environment joint debugging was successful (calling a simple cloud function returns the expected result)
• [ ] Sentry configuration verification: manually throw a test error to confirm that the Sentry background receives the event

FAQ

Step 2: Page UI development (WXML/WXSS + components)

⏱ Estimated time: 3-5 days (depending on the number of pages) 🎯 Goal: Use AI to complete the UI structure and style development of all pages of the mini program ⚠️ Prerequisites: The project skeleton is running normally and the design draft is ready

Operation instructions

UI development is the longest time-consuming step in mini program development. AI can significantly accelerate WXML template writing, WXSS style generation and UI component encapsulation.

Specific operations

1. Use v0 / Claude Artifacts to generate UI prototype

   Enter the design draft screenshot or requirement description into v0:

   "Please generate a product list page UI for a WeChat applet, including: top search bar, category tab switching,
   Product card (picture, title, price, sales volume), load more at the bottom. Output WXML+WXSS code. "

   Copy the generated WXML/WXSS to the corresponding page file and fine-tune it according to the actual design draft.

2. AI-assisted component development

   Develop custom components using the Agent pattern of Cursor:

   Example of prompt word: "Create a product card component in the src/components directory of the current project
   ProductCard, requirements: use Taro component specifications, receive product data props, include lazy loading of images,
   Click to jump to product details, price label style (red background, white font)"

3. AI generates WXSS style

   For complex styling needs, use GitHub Copilot's inline suggestions for speedup:

   /* Enter comments for Copilot to generate */
   /* Flex layout, aligned on both ends, 2-column grid, spacing 20rpx, rounded card, shadow effect */
   .grid-container {
    display: flex;
    flex-wrap: wrap;
    justify-content: space-between;
    padding: 10rpx 20rpx;
   }
   .grid-item {
    width: 345rpx;
    margin-bottom: 20rpx;
    border-radius: 16rpx;
    box-shadow: 0 4rpx 12rpx rgba(0, 0, 0, 0.06);
   }

4. Handling platform difference styles

   The style performance of different platforms is different (such as the performance of WeChat's rpx unit in the Douyin applet). Use Claude to handle the adaptation:

   "The WeChat applet uses rpx for adaptation, and the Douyin applet uses rpx but the base width is different.
   Please explain the difference in rpx calculation between the two platforms and give a compatible style of writing. "

5. UI component library integration

   Introduce Vant Weapp / Ant Design Mini / WeUI and get started quickly with AI assistance:

   "I use Vant Weapp to develop WeChat applet, please tell me how to introduce Button component and Dialog component,
   And provide usage sample code (WXML+JS). "

Verification method

• [ ] All page UIs render normally in WeChat developer tools
• [ ] Communication between components is normal (parent passes props, child triggers events)
• [ ] Lazy loading of images takes effect, and the placeholder image is displayed normally during network requests.
• [ ] The layout of the page is normal on both iPhone SE (small screen) and iPhone 14 Pro Max (large screen)
• [ ] Passed real-device debugging (connected to real-device preview, no white screen/disordered layout)

FAQ

Step 3: Logical interaction and API docking

⏱ Estimated time: 3-7 days 🎯 Goal: Use AI to assist in completing page interaction logic, API docking, status management and business data processing ⚠️ Preconditions: The UI page skeleton is completed and the backend API documentation is ready

Operation instructions

Logical interaction is the core of the mini program - data request, page status management, user operation feedback, payment docking, etc. The key role of AI in this link is to generate standardized API call code, handle boundary conditions and exception flow.

Specific operations

1. AI generated API service layer

   Create an API package file under src/services/ and use Claude to generate a standardized template:

   "Please help me generate the API service layer code of a small program. The requirements are: based on Taro.request encapsulation,
   Supports request/response interceptors, unified error handling, automatic Token injection, and timeout setting of 10 seconds.
   Interface list: user login, product list, product details, ordering, payment. "

2. State management implementation

   Based on the technology selection (Taro uses Redux/Zustand, uni-app uses Pinia/Vuex), use AI to generate Store code:

   "Please use Zustand to implement a shopping cart status management Store, including: adding products (quantity accumulation),
   Delete products, modify quantity, clear shopping cart, and calculate total price. Requires TypeScript type definitions. "

3. Complex interaction logic

Use the Agent pattern of Cursor to handle complex interactions:

   Prompt word: "Implement the shopping cart page in pages/cart/index.tsx,
   Functions include: increase or decrease in product quantity (with animation), sliding to delete, select all/inverse selection,
   Calculate the total price in real time and jump to the settlement button. Use Taro's API. "

4. Payment docking

   Payment is the most sensitive business aspect of mini programs. After using AI to generate code, the process logic must be manually reviewed.

   "Please generate the front-end docking code process for WeChat applet payment: Get openid → Call the back-end unified ordering interface →
   Call up WeChat payment → handle payment success/failure callbacks. Full error handling is required. "

   ⚠️ Access control: After the payment process is generated, it must be manually reviewed line by line, especially the three nodes of amount calculation, signature verification and callback processing. Use Sentry to monitor the abnormal rate of the payment interface.

5. Boundary conditions and error handling

   Use ChatGPT to conduct "adversarial testing" of existing logic:

   "Please review the following shopping cart logic code and point out all possible edge cases:
   Users click quickly, recover after network disconnection, insufficient product inventory, concurrent requests, and token expiration. "

Verification method

• [ ] All API requests return normally, and all three states of Loading/Error/Empty are processed
• [ ] Token automatic refresh mechanism takes effect (automatically refreshes and replays the request after expiration)
• [ ] The payment process has been tested in both developer tools and real devices.
• [ ] After the page data is updated, the UI is refreshed in time without data flickering.
• [ ] Display a friendly prompt page when the network is disconnected

FAQ

Step 4: Cloud development and back-end logic

⏱ Estimated time: 2-5 days 🎯 Goal: Use AI to assist in completing cloud functions, database operations, file storage and other functions developed by WeChat Cloud ⚠️ Prerequisites: The WeChat cloud development environment has been opened and the environment ID has been obtained

Operation instructions

WeChat Cloud Development (CloudBase) provides serverless back-end capabilities: cloud functions (Node.js running environment), cloud database (MongoDB-like), and cloud storage (CDN file management). AI can significantly reduce the time of writing template code for cloud functions, writing database aggregation queries, and configuring permission rules.

Specific operations

1. AI generated cloud function

   Use Claude to generate standardized cloud function code:

   "Generate a WeChat cloud function getUserInfo, function:
   1. Query user information from the database users collection
   2. Input parameter: openId (required)
   3. Return: basic user information (nickname, avatar, points, membership level)
   4. Error handling: 404 is returned if the user does not exist, 500 is returned if the database exception occurs
   5. Log output: record the call source and time"

2. Database operation optimization

   AI assists in writing complex database query statements:

   "Please generate a cloud function to implement paging query of the product list, which requires aggregation operations:
   1. Filter by category (categoryId)
   2. Sort by sales volume
   3. Paging (page/size)
   4. Return the total number of items total
   5. Only return listed products (status === 'online')"

3. Cloud Storage and Image Processing

   Use AI to generate cloud functions for avatar uploading and file management:

   "The complete process of generating a user's avatar upload: select the image in the applet → call cloud storage to upload →
   Return fileID → Update the URL into the database user record. Includes file size limit (within 2MB) and format verification. "

4. Security Rule Configuration

   The security rules of cloud databases are easily overlooked security risk points. Use ChatGPT to verify:

   "Help me review the following cloud database security rules and find out the permission out-of-bounds issues:
   [Rule content]. Scenario: Product data can be read by all users, only administrators can write, and user data can only be read and written by the user. "

5. Timed Trigger

   AI generates cloud functions that are executed regularly (such as cleaning up expired orders every day):

   "Generate a regularly triggered cloud function cleanExpiredOrders, which will be executed at 2 a.m. every day.
   Query the orders in the orders collection that have not been paid for more than 24 hours, change their status to 'expired' and release the inventory. "

Verification method

• [ ] All cloud functions are deployed successfully, and local calls return expected results.
• [ ] Database security rules passed the test in the developer tools security rule simulator
• [ ] Cloud storage upload/download is normal, and the image URL can be rendered in the mini program.
• [ ] Scheduled triggers are executed as planned, and the logs can be checked
• [ ] The cloud function call duration is within 1000ms (WeChat limits a single call to no more than 20s)

FAQ

Step 5: Multi-terminal adaptation and cross-platform development

⏱ Estimated time: 2-4 days (first adaptation) 🎯 Goal: Adapt the mini program to other platforms such as Alipay/Douyin/Baidu, so that one set of code can run on multiple terminals ⚠️ Prerequisites: The WeChat mini program has been developed and passed basic testing

Operation instructions

Multi-terminal adaptation is the most challenging aspect in mini program development. Different platforms have different component naming, API differences, style presentation, and review rules. Using Taro or the uni-app cross-end framework can reduce most of the adaptation work, but there is still platform-specific code to deal with.

Specific operations

1. Cross-end framework configuration

   Use Cursor to configure multi-terminal compilation:

   Prompt word: "Please configure compilation support for the Alipay applet for the Taro project.
   Including: installing Alipay dependencies, configuring the Alipay format of project.config.json,
   Create an Alipay-specific conditional compilation example. "

2. Conditional compilation processing platform differences

   Use AI to identify and flag platform-differenced code:

   "The following code runs normally in the WeChat applet. Please change it to Taro conditional compilation format.
   Also identify code segments that require platform differences and add comments:
   [code content]"

3. API difference adaptation

Each platform has different API methods (WeChat wx.xxx, Alipay my.xxx, Douyin tt.xxx), and AI is used to establish the mapping layer:

   "Please create a Taro plug-in or utility function to uniformly handle the following platform differences:
   1. Login API: wx.login → my.getAuthCode → tt.login
   2. Payment API: wx.requestPayment → my.tradePay → tt.pay
   3. User information: wx.getUserProfile → my.getAuthUserInfo → tt.getUserInfo"

4. Platform-specific review materials

   Use ChatGPT to prepare materials according to the review requirements of different platforms:

   "I am adapting the WeChat applet 'xxx' to the Alipay applet. Please help me generate:
   1. Function description document of Alipay mini program
   2. Privacy Agreement Update Comparison Table (WeChat version → Alipay version)
   3. Checklist of common reasons for rejection by each platform review"

5. Multi-terminal compatibility test

   Compile and preview separately in the developer tools of each platform, and AI assists in troubleshooting differences:

   "The page scrolling in the Alipay mini program is stuck. Please check the following code for possible reasons:
   [code snippet]. Note that the Alipay applet does not support some properties of scroll-view. "

Verification method

• [ ] The developer tools for the three platforms of WeChat/Alipay/Douyin are all compiled and passed.
• [ ] Core functions (login, payment, product list) are available on all three ends and perform consistently
• [ ] API calls on each platform are normal and there is no xxx is not defined error.
• [ ] The package size of each platform is within the limit.
• [ ] Passed the real machine test (at least one real machine per platform)

FAQ

Step 6: Testing, performance optimization and review

⏱ Estimated time: 2-4 days 🎯 Goal: Complete functional testing, performance analysis and review and release through AI assistance ⚠️ Prerequisites: Function development completed, multi-terminal adaptation completed

Operation instructions

Testing and optimization are the last steps before going online. The value of AI in this link lies in quickly generating test cases, identifying performance bottlenecks and preparing audit materials.

Specific operations

1. AI generates test cases

   Use Claude to generate test cases for key business logic:

   "Generate Jest unit test cases for the following shopping cart logic, covering:
   Add products normally, add duplicate products (quantity is accumulated), remove products, clear shopping cart,
   Empty shopping cart settlement, insufficient product inventory, and network abnormalities.
   [Shopping Cart Store Code]"

2. Performance Analysis

   Use the Performance Panel (Audits) of WeChat Developer Tools to analyze loading performance. AI-assisted optimization:

   "The first rendering of my WeChat applet took 2.5 seconds (Audits score 65). The following is my page code.
   Please analyze the reasons why the first screen loads slowly and provide optimization solutions:
   1. Reduce the number of synchronization requests
   2. Images use WebP format
   3. Enable subpackage loading
   4. Reduce setData frequency and size"

3. Package volume optimization

   Use ChatGPT to analyze and optimize package size:

   "The size of my mini program main package is 1.8MB, which is close to the 2MB limit. Please analyze which of the following directories can be optimized:
   - Are the images compressed?
   - Are there any unused components/pages?
   - Is the third-party library clipping?
   - Is tree-shaking enabled?
   Specific optimization steps are given. "

4. Sentry error monitoring verification

   Use Sentry to monitor exceptions before going online:

   • Upload the applet code to the test environment
   • Conduct multi-scenario testing (normal process, abnormal operation, weak network environment)
   • Check the errors captured by Sentry background and AI-assisted analysis of root causes

5. Review and prepare materials for listing

   Use AI to quickly generate review materials:

   "I am submitting the WeChat applet 'xxx' v1.0 for review. Please help me generate:
   1. Function description document (within 100 words)
   2. Experience account (test account and password)
   3. Contents of the privacy agreement (in compliance with the "WeChat Mini Program Platform Operation Specifications")
   4. Common reasons for rejection and self-check items"

   ⚠️ Access control: Before submitting for review, manual confirmation is required:

   • There is no risk of violation of paid purchases without using IAP payment for mini programs
   • The user privacy agreement has been updated and displayed correctly
   • All pages of the mini program can be accessed normally
   • The backend management terminal has configured user feedback channels

Verification method

• [ ] Unit test coverage > 80%, all key business processes are covered
• [ ] WeChat Mini Program Audits Rating ≥ 85
• [ ] First screen loading time ≤ 1.5 seconds (real device test)
• [ ] Sentry test environment has no Unexpected Error
• [ ] Main package size < 1.5MB (leave enough margin)
• [ ] The review materials are complete and have passed the internal review of the team

FAQ

5. Expected results

Deliverables List

Performance indicators

Acceptance criteria

• [ ] The mini program can be opened and used normally on WeChat/Alipay/Douyin.
• [ ] Core business processes (login→browse→order→pay→after-sales) run through the whole link
• [ ] All cloud functions are deployed online and monitored normally, with no 500 errors.
• [ ] There are no unprocessed major errors within 48 hours after Sentry error monitoring goes online.
• [ ] The package size at each end is within the limit (main package ≤ 1.5MB)
• [ ] The review materials have been reviewed by the platform and successfully put on the shelves
• [ ] The project documentation is complete and the team can maintain it independently after handing it over to operation and maintenance.

6. Frequently Asked Questions and Troubleshooting (FAQ)

Q1: I have no experience in developing small programs. Can I use AI to make a small program from scratch?

A: You can get started, but it is not recommended to rely entirely on AI. AI can help you generate the first version of code, understand API usage, and troubleshoot errors, but mini program development involves architectural concepts unique to the WeChat platform (life cycle, subcontracting, custom component communication, setData performance, etc.). Zero-based developers need to spend at least 1-2 weeks learning basic concepts (WXML/WXSS syntax, JS basics, mini program life cycle), otherwise problems with AI-generated code will not be independently troubleshooted. Recommended path: First complete WeChat's official "Quick Start" tutorial → Use AI to assist in developing 1-2 simple pages → Gradually expand to the entire process.

Q2: Will the code generated by AI infringe copyright or violate WeChat platform specifications?

A: The ownership of the code generated by AI belongs to the developer, and there are no copyright issues. However, you need to pay attention to the following red lines: When the content generated by AI involves WeChat platform specifications, it must be reviewed manually - especially the payment process (you cannot bypass WeChat Pay and use third-party payment), user privacy (privacy agreement must be displayed in accordance with the "WeChat Mini Program Personal Information Protection Specifications"), and category selection (categories such as medical/finance require additional qualification documents). It is recommended that after using AI to generate review materials, you should go through them manually against the latest version of the "WeChat Mini Program Operation Specifications".

Q3: Is the security and stability of using AI-generated code reliable?

A: It needs to be treated in different levels. The quality of AI-generated simple UI codes (lists, forms, display pages) can be used directly; data processing codes involving money transactions (payment, order amount calculation, refund logic) must be reviewed by two people; sensitive information such as API keys and Tokens must not be hard-coded in AI-generated code and must be injected through environment variables or cloud development configuration. It is recommended to add code security scanning (such as SonarQube) to the CI process and set critical alerts in Sentry.

Q4: What should I do if the three-terminal unified code generated by AI during multi-terminal adaptation reports an error when running on a certain platform?

A: This is the most common scenario for using cross-end frameworks. Troubleshooting process: (1) Confirm whether the developer tool version of the platform supports the API used (some old versions do not support new features); (2) Use conditional compilation of Taro/uni-app to write separate compatible code for the platform; (3) Paste the error message to Claude or ChatGPT for analysis and resolution. A large amount of practical experience shows that 90% of platform compatibility issues can be solved within 30 minutes with the assistance of community documentation and AI.

Q5: What else can AI help with after the mini program is released?

A: The continued value of AI after release is reflected in: (1) Operational content generation: Use AI to write marketing copy, event pages and push copy in the mini program; (2) Problem troubleshooting: Paste Sentry error logs to AI to quickly locate the root cause; (3) Version iteration: Continue to reuse the AI workflow for new version feature development; (4) Data Analysis: AI-assisted analysis of data from the mini-program data analysis platform, generating operational reports and improvement suggestions; (5) Customer Service Automation: Connect to the AI customer service plug-in to automatically respond to common user questions.

Q6: What is the total cost of developing a complete mini program with AI assistance?

A: Taking an independent developer as an example: the monthly tool cost is about $96 (Cursor Pro $20 + ChatGPT Plus $20 + Sentry $26 + Claude Pro $20 + GitHub Copilot $10); WeChat certification fee is ￥30 (annual fee); cloud development fees are billed on a volume basis, which is about ￥50-200 per month in the initial stage. The total monthly cost is about ￥700-1000 (including tool subscription + cloud resources). Compared with hiring a full-time small program developer (monthly salary 15K-30K), the cost of AI-assisted solutions is about 1/20-1/30 of the hiring cost.

Q7: How to choose between WeChat cloud development and traditional backend?

A: Basis for selection: Decided by project size and complexity. Cloud development is suitable for: small and medium-sized applications (users <100,000), prototype verification period, and teams without full-time backend. The traditional backend is suitable for: complex businesses that require customized authentication/database/middleware, existing backend teams, and fine control requirements for the database. AI recommendation: Use cloud development for quick verification in the initial stage. When the business grows and requires a customized backend, it can be smoothly migrated to Tencent Cloud CloudBase application hosting or self-built servers.

7. Advancement and expansion

7.1 Mini program + AI agent

After completing basic development, AI capabilities can be embedded in the mini program:

• AI Customer Service Assistant: Connect to large model API (such as Claude or ChatGPT API) to implement intelligent question and answer robots
• AI Image Recognition: Call cloud development AI capabilities or third-party APIs (Tencent Cloud AI, Baidu AI) to realize object recognition and OCR recognition when taking photos
• AI Recommendation Engine: Personalized recommendations based on user behavior data
• AI copywriting generation: Automatically generate product descriptions and marketing copywriting on the merchant side

7.2 Automated pipeline upgrade

• GitHub Actions + WeChat CI: Automatic compilation, static analysis, unit testing, and uploading trial version after pushing the code
• AI Assisted Code Review: Use AI to automatically review the code in PR to detect potential bugs and performance issues
• Automated screenshots: Use Playwright + WeChat developer tool CLI to automatically generate multi-end screenshots for operating materials

7.3 From single platform to omni-channel

• Starting point: WeChat applet → Verify business model
• Extension: Alipay + Douyin mini program → Covering mainstream user groups
• H5 synchronization: Taro compiles H5 version → embed in public account/external channel
• App packaging: Use Taro's RN compilation capability or Flutter applet solution to generate iOS/Android native applications

7.4 Performance and Growth

• Performance Monitoring Automation: Use Sentry Performance to track page load time and API response time, and set P95 alarm threshold
• A/B Test: Integrate multi-link jumps and exposure points, AI-assisted analysis of experimental data
• User Growth Model: AI-assisted analysis of user retention, sharing rate and conversion rate, and output optimization suggestions
• Automated Operation: Based on user behavior data (combined with cloud development database), use cloud functions to regularly send template messages and coupons

7.5 Security and Compliance Extensions

• Code Security Audit: Generate security scanning CI tasks, using ESLint security rule set
• Data Compliance: AI-assisted generation of the latest version of the privacy agreement and user agreement that meets the requirements of each platform
• Penetration Test Assistance: AI assists in analyzing common security issues (XSS, SQL injection, unauthorized access) and providing repair solutions