Virtual Carnival: A Pure AI Development Experiment with Zero Human-Written Code

This article introduces the Virtual Carnival project—a web application experiment fully generated by AI with zero human-written code. The project uses Claude Opus 4.7 and the Perplexity framework, implemented via a 13,000-character prompt, aiming to explore the limits of "Vibe Coding", demonstrate the boundaries of current AI programming capabilities, and provoke thoughts on the future form of software development.

From 2024 to 2025, the programming capabilities of large language models improved rapidly, and "Vibe Coding" began to gain popularity: developers no longer write code line by line but use natural language to describe requirements for AI to generate complete implementations. The Virtual Carnival project pushes this concept to the extreme—the entire web application is fully generated by AI with no human-written code, to verify the feasibility of pure AI development.

Experiment Objectives: Verify whether a fully functional web application (virtual carnival scenario, involving UI interaction, backend logic, multimedia display, etc.) can be built with only AI assistance and zero human-written code. Technology Stack:

• Claude Opus 4.7: Responsible for main code generation and architecture design
• Perplexity Computer Framework: Enhances context understanding and tool calling
• 13,000-character natural language prompt: Contains detailed descriptions of application overview, functional requirements, technical constraints, etc.

Development Process:

1. Initial Prompt Engineering: Based on a detailed 13,000-character prompt (including application overview, functional requirements, technical constraints, design specifications, quality standards).
2. Iterative Adjustment: The initial version is not perfect; corrections are made via natural language feedback, and the human role shifts to reviewer and guide. Model Comparison:

   Stage	Traditional Development	Virtual Carnival Model
   Requirements Analysis	Manual document writing	AI-assisted clarification and refinement
   Architecture Design	Manual design	AI-generated and explained
   Code Implementation	Manual writing	AI generates all code
   Testing & Debugging	Manual execution	AI-assisted diagnosis and repair
   Deployment & Operation	Manual configuration	AI-guided automation

Possible Technical Architecture:

• Frontend: React/Vue/Angular + Tailwind CSS (responsive design)
• Backend: Node.js/Express or Python/FastAPI + Database (SQLite/PostgreSQL/MongoDB)
• Deployment: Static hosting (Vercel/Netlify), containerization (Docker), or cloud services (AWS/GCP/Azure) Code Quality:
• Advantages: Follows best practices, unified style, complete documentation, comprehensive security considerations
• Potential Issues: Over-engineering/under-engineering, insufficient handling of edge cases, lack of performance optimization, maintainability to be verified.

Definition of Vibe Coding: Promoted by Andrej Karpathy, it refers to developers conversing with AI instead of writing code directly, focusing on "what is wanted" rather than "how to implement it", and accepting a certain degree of black box. Efficiency vs. Quality Trade-off:

• Advantages: Fast development speed, low learning curve, strong exploratory nature, reduces repetitive work
• Challenges: Poor maintainability, difficult debugging, accumulation of technical debt, skill degradation Industry Impact:
• Role Transformation: From code writers to AI collaborators and system designers
• Skill Changes: Prompt engineering becomes core, code review ability is more important
• Positive Aspects: Lowers entry barriers, accelerates innovation
• Concerns: Code quality/security, IP definition, over-reliance.

Insights: Pure AI development is feasible under specific conditions but not applicable to all projects; success still requires humans to have clear requirement expression, technical review, problem-solving abilities, and critical thinking. Best Practices: Detailed pre-planning, iterative development, continuous review, and retaining human control. Future Trends:

• Short-term: AI code accuracy improves, more human-AI collaboration tools emerge
• Mid-term: AI handles complex architectures, automated testing/debugging is enhanced
• Long-term: Software development paradigm shifts, natural language becomes the main interface, humans focus on creativity and decision-making.