Task: Create a Global Unique Identifier Generator (GSID)
Purpose: Explore different strategies for AI-assisted development
Original Project: metarhia/metautil/tree/gsid-ai
This project demonstrates three different approaches to AI-assisted development by implementing a Global Unique Identifier (GSID) generator. Each approach represents a different strategy for working with AI tools, from basic prompting to structured technical specifications.
- 0-Simple/ - Simple Math.random() implementation
- 1-Prompt/ - Short prompt in vibe coding style
- 2-Chat-steps/ - Step-by-step chat with AI in vibe coding style
- 3-Tech-specs/ - Technical specification implementation
- 4-By-example/ - Browser-compatible implementation
- 5-Manual/ - Manually optimized implementation (Server + Web)
- benchmark.js - Performance comparison tool
- README.md - This file
Basic vibe coding without deep understanding of the task or development process.
Characteristics:
- Minimal task description
- Limited understanding of AI-generated code quality
- Basic implementation without optimization
- Focus on quick results rather than best practices
Human Developer Time: ~5-10 minutes
- Task Definition: 2 minutes (basic prompt)
- Code Review: none
- Testing: 3 minutes (basic validation)
Iterative vibe coding with desire for improvement but limited technical depth.
Characteristics:
- Interactive development process
- Attempts to improve implementation
- Limited understanding of code quality and optimization
- Focus on incremental improvements
Human Developer Time: ~20-30 minutes
- Initial Task: 5 minutes
- Code Review & Refactoring: 5 minutes (SRP violations, unnecessary methods)
- Architecture Decisions: 5 minutes (class structure, method removal)
- Testing & Validation: 5 minutes (comprehensive testing)
Professional development workflow with AI as a regular team member.
Characteristics:
- Detailed technical specifications
- Clear requirements and constraints
- Focus on performance, security, and best practices
Human Developer Time: ~2-3 hours
- Requirements Analysis: 30 minutes (detailed specifications)
- Technical Design: 45 minutes (performance targets)
- Implementation Planning: 30 minutes (optimization strategies)
- Testing Strategy: 20 minutes
- Performance Analysis: 30 minutes
| Approach | Time | Quality | Performance | Method | Cost |
|---|---|---|---|---|---|
| Short Prompt | ~5 min | Basic | 1.28M IDs/sec | Basic AI prompt | $5-10 |
| Step-by-Step Chat | ~20 min | Good | 1.22M IDs/sec | Iterative AI development | $10-20 |
| Technical Specification | ~2 hours | Excellent | 5.27M IDs/sec | Detailed AI specifications | $150-250 |
| Manual Optimization | ~30 min | Excellent | 10.00M IDs/sec | Manual optimization | $25-50 |
| Expert Traditional | ~18 min | Excellent | 5M+ IDs/sec | Optimized implementation | $25-50 |
Key Insights:
- Manual optimization achieves the best performance (10.00M IDs/sec) with moderate time investment
- Technical specification provides excellent performance (5.79M IDs/sec) with significant time investment
- Step-by-step chat provides 4x time investment for moderate quality improvement
- Short prompt is fastest but produces lowest quality results
- Time investment directly correlates with code quality and performance
Based on comprehensive benchmarking, the implementations show significant differences:
| # | Implementation | Performance | Memory Usage | Size | Entropy | Theoretical |
|---|---|---|---|---|---|---|
| 5 | Manual (Server) | 10.00M IDs/sec | 69MB | 24 chars | 6.000 bits/char | 144 bits |
| 3 | Tech-specs | 5.79M IDs/sec | 86MB | 24 chars | 6.000 bits/char | 144 bits |
| 4 | By-example | 1.43M IDs/sec | -118MB | 24 chars | 6.000 bits/char | 144 bits |
| 5 | Manual (Web) | 1.64M IDs/sec | 99MB | 24 chars | 6.000 bits/char | 144 bits |
| - | UUID v4 | 2.05M IDs/sec | 194MB | 36 chars | 4.050 bits/char | 122 bits |
| 0 | Simple | 2.02M IDs/sec | 97MB | 24 chars | 4.987 bits/char | 120 bits |
| 1 | Prompt | 1.24M IDs/sec | 83MB | 27 chars | 4.770 bits/char | 162 bits |
| 2 | Chat-steps | 1.21M IDs/sec | 21MB | 27 chars | 5.190 bits/char | 162 bits |
Key Findings:
- Manual optimization (Server) produces the most efficient implementation - 10.00M IDs/sec with excellent memory efficiency (69MB)
- Tech-specs approach provides excellent performance (5.79M IDs/sec) with good memory efficiency (86MB)
- By-example implementation provides excellent browser compatibility (1.43M IDs/sec) with perfect entropy and negative memory delta (-118MB)
- Manual optimization (Web) provides excellent browser compatibility (1.64M IDs/sec) with perfect quality
- UUID v4 shows good performance (2.05M IDs/sec) but high memory usage (194MB)
- Simple implementation shows moderate performance (2.02M IDs/sec) with reasonable memory usage (97MB)
- All implementations maintain perfect collision resistance (0% collision rate)
- Node.js 24 provides native Web Crypto API support for .mjs files
- Cursor: claude-4-sonnet, claude-3.5-sonnet, gpt-4.1, o3, gemini-2.5-pro
- VS Code with Copilot: gpt-4.1, claude-4-sonnet, gemini-2.5
- ChatGPT: gpt-4o, o4-mini-high, gpt-4.1, gpt-4.5
- Clone the repository:
git clone <repository-url> - Open folder:
cd CodeWithAI - Install dependencies:
npm i - Run tests:
npm t - Run benchmark:
node benchmark.js
The browser-compatible implementation demonstrates adapting Node.js code for web environments:
- Technology: ES Modules with Web Crypto API
- Key Features:
- Pure Web API usage (
crypto.getRandomValues()) - No Node.js dependencies
- Cross-platform compatibility
- Same character set and algorithm as Tech-specs
- Pure Web API usage (
- Performance: 1.45M IDs/second with 0% collision rate
- Size: 24 characters (same as Tech-specs)
- Usage:
// Browser import { generateID } from './id.mjs'; const id = generateID(); // Node.js (native support in v24+) node benchmark.js
The manually optimized implementation demonstrates exceptional performance through careful optimization:
- Technology: Node.js with optimized crypto usage + Browser-compatible ES modules
- Key Features:
- Pre-allocated buffers for maximum performance
- Lookup table optimization with
& 0x3fbit masking - Variable length support without performance degradation
- Perfect entropy utilization (100% efficiency)
- Cross-platform compatibility (Node.js + Browser)
- Dual implementations: Server (Node.js) and Web (Browser)
- Performance: 10.00M IDs/second (Server) - fastest implementation
- Performance: 1.64M IDs/second (Web) - excellent browser compatibility
- Memory Usage: 69MB (Server) / 99MB (Web) - excellent memory efficiency
- Size: 24 characters (compact and efficient)
- Usage:
// Node.js (Server) const { generateId } = require('./5-Manual/gsid.js'); const id = generateId(); // Browser (Web) import { generateId } from './5-Manual/gsid.mjs'; const id = generateId();
- Technical specifications produce the most efficient and maintainable code with AI
- Iterative development can improve results a little but requires technical expertise
- Basic prompting often leads to functional but suboptimal implementations
This project is part of the original metarhia/metautil repository and follows its licensing terms.