Spring Festival Break: Building an AI App with DeepSeek + ClinePRO, 12x Efficiency Boost

This Spring Festival break, I developed a complete product using DeepSeek + AISE ClinePRO. Total coding time: 20 hours. AI handled project setup, coding, testing, and debugging - I didn’t write a single line of code. Traditional manual development would have taken at least 240 hours. Rough estimate: 12x efficiency boost.

Have you seen Tsim Sha Tsui at 5 AM?
Have you witnessed Chinatown at 6 AM?
Have you watched AI write code during sunrise at Sentosa?

This Spring Festival break, I experienced all of the above. Every morning from 5-8 AM, I developed a complete product using DeepSeek + AISE ClinePRO. Total coding time: 20 hours, with most time spent on problem decomposition and optimizing context for AI. AI handled the entire project setup, coding, testing, and debugging - I didn’t write a single line of code.

Traditional manual development would have required 3 developers working full-time for 2 weeks (240 hours). Rough estimate: 12x efficiency boost.

Seeing these numbers, many might exclaim that programmers will become obsolete. Let me assure you, programmers won’t disappear - they’ll become more important, more specialized, and more powerful. For detailed reasoning, see the video demonstration and analysis at the end of this article.

In this series, I’ll share the entire development process, covering product design, architecture, tool selection, and prompt engineering details. I’ll also open-source some code for learning and reference. Notably, I conducted comparative tests between DeepSeek V3/R1 and Claude 3.5 Sonnet, and I’ll share insights on DeepSeek’s enterprise-level coding capabilities and practical techniques.

Article Series

This series will be divided into the following parts, continuously updated to provide deep insights into AI-assisted development and building complete projects from scratch:

  • Introduction - Covers Code2Docs.ai’s product design and background, showcasing current results and introducing architecture and development environment. I’ll highlight key efficiency boost points in AI-assisted development.
  • Project Creation & Main Flow Development - Details using AI to create code projects from scratch and implement main workflows. Covers Python API development, GitHub Action creation, and frontend API integration.
  • GitHub Action Development - Core functionality implementation, involving complex Linux scripting like JSON file generation and parsing. Shares effective issue writing and task planning adjustments.
  • Multilingual Support & Layout Adjustments - Simplifies multilingual interface implementation and content translation. AI completes in minutes what might take humans 3 days.
  • Generation Process Monitoring - Complex task of creating status pages for real-time job progress tracking. Involves backend API implementation and frontend data rendering.
  • Homepage & Documentation Library Optimization - Code structure refactoring and component extraction. AI plays a key role in optimization tasks.
  • Miscellaneous - Automating documentation tasks like About pages, privacy policies, and terms of service using AI templates.

Development continues with Docker deployment and CI/CD pipeline setup, where AI continues to excel.

Background

In December, DeepSeek launched V3, showing programming capabilities approaching Claude 3.5 Sonnet. On January 20, DeepSeek released R1, further enhancing programming accuracy and stability. With AISE Workspace’s Code2Docs capabilities ready, I wondered: Could I complete a full app development using DeepSeek + ClinePRO during Spring Festival?

Product Showcase

Talk is simple, show me the code and app.

Code2Docs.ai is live. Scan the QR code or visit: https://code2docs.ai

Screenshot of DeepSeek-generated “About” page:

Code2Docs is an innovative documentation generator that transforms your code repositories into comprehensive, well-structured documentation. Our AI-driven system analyzes your codebase to create clear, accurate, and maintainable documentation that evolves with your project.

Example documentation library generated by Code2Docs.ai. The tool scans codebases, uses AST for analysis, and leverages DeepSeek V3 for code interpretation. Current documentation is technical, but we plan to enhance it with API docs, code examples, and business context. Future support includes Word and PowerPoint formats.

Product Design

On my flight from Beijing to Hong Kong, I designed Code2Docs.ai’s workflow:

  1. Codebase to Documentation: Already implemented in AISE Workspace via CLI.
  2. Automation System: Simplest implementation via GitHub Action triggered by API.

Three main modules:

  1. Frontend Website: Built from scratch for desktop and mobile.
  2. Backend API: Added to AISE CLI for Code2Docs capability.
  3. Automation System: Asynchronous multi-process job execution via GitHub Action.

Simplified scenario diagram:

Detailed workflow:

  1. User Input: Git repository URL on homepage.
  2. GitHub Action Trigger: Pre-configured GitHub Action execution.
  3. Documentation Generation & Logging:
    • Extract organization and repository names.
    • Maintain workflow_runs.json.
    • Call AISE Workspace Code2Docs capability.
    • Push generated documentation to new Git repository.
  4. Result Statistics: Display generation statistics from workflow_runs.json.

Public GitHub Action repository: https://github.com/code2docs-ai/code2docs-ai-core

Efficiency Boosters

Optimizations revealed AI’s strength in handling repetitive tasks. Key efficiency improvements:

Multilingual Processing: From Tedious to Efficient

Multilingual implementation, while mature, is time-consuming. AI automated detection and implementation of LanguageContext.tsx, significantly reducing workload.

Mobile Optimization: Simplified Development

Responsive design, traditionally complex, was fully automated by AI. All styles and layouts were AI-generated, with surprisingly aesthetic results. Completed 10 frontend pages in 5 days (3 hours/day).

Domain Knowledge Handling

AI automated generation of legal documents (privacy policy, terms of service) by reading templates and adapting to project specifics, saving significant time and reducing legal risks.

Project Summary

Development timeline:

  • Start: January 31 (3rd day of Spring Festival)
  • MVP Completion: February 6 (9th day)
  • Total Hours: <20 (5-8 AM daily)

Components:

  • Frontend: Vite + React + Tailwind
  • Backend: Python
  • Automation: GitHub Action

Traditional development estimate: 3 developers × 2 weeks = 240 hours AI-assisted efficiency: 12x boost

AI not only saved time but also made development more enjoyable by automating tedious tasks, allowing focus on core logic and design.

AI Toolset

Key tools used:

Models

  • DeepSeek V3 & R1: Primary models, mostly V3 with R1 for complex tasks.
  • Claude 3.5 Sonnet: Reference model.
  • Qwen 2.5 Coder 32b Instruct: Reference model.

IDE

  • Visual Studio Code

AI Coding Tools

  • AISE ClinePRO: Enterprise-grade multi-agent coding tool based on cline.
  • AISE SmartCode: Code completion and smart dialogue tool.
  • AISE SmartChat: Technical research and design documentation tool.
  • GitHub Workspace: Online AI multi-agent coding tool for GitHub Action development.

All AI coding tools use DeepSeek V3 as the underlying model.

Video demonstration of ClinePRO’s multi-agent coding capabilities:

AI Era: Real Developers Won’t Be Obsolete

The development process highlights the new paradigm of human-AI collaboration. Key developer capabilities in the AI era:

  1. Deep Understanding of AI Logic: Monitoring and correcting AI output.
  2. Stronger Technical Foundation: Understanding underlying principles for code validation and optimization.
  3. Broader Knowledge: Cross-domain expertise for better AI utilization.
  4. More Responsible Work Ethic: Final responsibility remains with human developers.

AI won’t replace developers but will become a powerful assistant, freeing us from repetitive tasks and enabling focus on creative and strategic work. Real developers will play a more crucial role in the AI era.

Embrace this new paradigm, enhance your technical depth and breadth, and become an AI-era developer who can effectively harness AI’s potential. This is the true direction of future technological development.