Building a WeChat Mini‑Program in 30 Minutes with an AI Coding Assistant

01. AI Programmer Overview

The author, lacking programming experience, explored whether AI could generate a web, mini‑program, or app and discovered that the Tongyi Lingma "AI programmer" mode, powered by DeepSeek V3 and the free, unlimited Qwen2.5‑Max model, can indeed produce complete code from natural‑language requests.

02. Model Capabilities

Qwen2.5‑Max, built on a massive Mixture‑of‑Experts (MoE) architecture with 200 trillion tokens of pre‑training data, outperforms open‑source models and matches or exceeds GPT‑4, Claude‑3.5‑Sonnet, and DeepSeek V3 on benchmarks such as MMLU‑Pro (knowledge), LiveCodeBench (programming), LiveBench (overall ability), and Arena‑Hard (human‑preference alignment).

03. Setting Up the Development Environment

The author installed the Tongyi Lingma plugin in Visual Studio Code, logged in via the Alibaba Cloud portal, and selected the desired model (Qwen2.5‑Max or DeepSeek V3) from the "AI programmer" panel.

04. Generating the Mini‑Program

By typing a natural‑language requirement—"Create a WeChat mini‑program that starts timing on a Start button, stops on an End button, and displays the elapsed time"—the AI generated all necessary source files. The author accepted the generated code and imported the project into the WeChat Developer Tools.

05. Building and Previewing

After importing, the author compiled the project in the WeChat Developer Tools, which produced a real‑time preview of the mini‑program UI.

06. Iterative Modification

When the initial output did not fully meet expectations, the author refined the request in plain language (e.g., adding a calendar view to show historical timing records). The AI updated the code accordingly, and the author simply accepted the changes.

07. Error Handling

If compilation errors appeared, the author copied the red error messages, pasted them into the AI prompt, and the assistant automatically corrected the problematic code.

08. Deployment

Once all code was finalized, the author uploaded the mini‑program, passed the review process, and obtained a functional WeChat mini‑program.

09. Future Enhancements

The author suggests adding background images (e.g., a panda before/after using the timer) and a poop‑recording feature that aggregates weekly reports.

10. Overall Assessment

The demonstration shows that Tongyi Lingma’s AI programmer can handle project scaffolding, code optimization, feature development, bug fixing, and refactoring, enabling users with zero coding background to build and iterate on applications quickly.