3 रिपॉजिटरी
Mini programs that leverage cloud hosting, serverless functions, databases, and storage for backend capabilities.
Distinct from Mini Program Development: Distinct from Mini Program Development: focuses specifically on the cloud backend integration aspect, not general mini program development.
Explore 3 awesome GitHub repositories matching mobile development · Cloud-Integrated Mini Programs. Refine with filters or upvote what's useful.
This repository is a demonstration project for building mini programs that run inside the WeChat ecosystem. It provides a complete development framework for creating lightweight, native-feeling services using declarative components, data binding, and platform APIs. The project showcases how to construct applications with a component-based UI architecture, file-based page routing, and a dual-thread rendering model where the logic layer operates in a JavaScript engine while the view layer renders through native WebView components. The demo illustrates integration with cloud services, including
Demonstrates cloud integration with serverless functions, database SDK, and storage for mini programs.
This project is a multi-gateway payment SDK that provides a unified API and abstraction layer for integrating multiple payment providers. It maps high-level payment operations to provider-specific API calls and standardizes diverse outputs through a unified response normalization system. The SDK supports multi-tenant configuration, allowing a single runtime instance to isolate credentials and settings for different business accounts. It features a plugin-based architecture that enables the addition of custom payment gateways through abstract class implementations. The capability surface cove
Handles order creation and refund requests specifically for users within mini-program environments.
This project is a comprehensive educational resource and tutorial handbook for building, training, and deploying machine learning models using TensorFlow 2. It serves as a structured learning guide covering core deep learning concepts, including neural network architectures, automatic differentiation, and tensor operations. The handbook provides technical guidance on optimizing execution efficiency through GPU memory management, distributed training, and model quantization. It also includes detailed manuals for constructing high-performance data pipelines and exporting models for production s
Provides technical guidance on embedding machine learning capabilities into mini-programs using GPU acceleration wrappers.