# 🤖 Robotics MCP WebApp - 2025 AI-First Development Standards
## Core Philosophy
- **Architecting**: Human-AI collaborative design sessions
- **Programming**: Agentic AI writes all code autonomously
- **Testing**: AI generates and runs all tests (human oversight only)
- **Quality**: AI maintains code standards and reviews changes
## AI Configuration
```json
{
"ai": {
"model": "claude-3.5-sonnet",
"temperature": 0.1,
"maxTokens": 4000,
"autonomyLevel": "high"
},
"workflow": {
"architecting": "human-ai-collaborative",
"programming": "ai-driven",
"testing": "ai-automated",
"review": "human-oversight"
}
}
```
## Code Standards (2025)
- **Language**: TypeScript 5.6+ (frontend), Python 3.12+ (backend)
- **Framework**: Next.js 16 App Router, FastAPI async
- **Linting**: Ruff (single tool replaces flake8, black, isort)
- **Testing**: AI-generated comprehensive test suites
- **Documentation**: AI-maintained inline docs and API specs
## Development Workflow
### Phase 1: Human-AI Collaborative Architecting
1. Human defines vision and requirements
2. AI analyzes technical feasibility
3. Human-AI iterate on architecture design
4. AI generates detailed technical specifications
5. Human reviews and approves final architecture
### Phase 2: AI-Driven Programming
1. AI receives approved architecture specifications
2. AI implements features autonomously
3. AI maintains code quality standards
4. Human reviews code changes and provides feedback
5. AI incorporates feedback and refines implementation
### Phase 3: AI-Generated Testing
1. AI analyzes code changes
2. AI generates comprehensive test suites
3. AI runs all tests continuously
4. AI analyzes failures and suggests fixes
5. Human reviews test results and approves
## File Organization
```
robotics-webapp/
├── backend/ # FastAPI backend (Python 3.12+)
├── src/ # Next.js frontend (TypeScript 5.6+)
├── docs/ # AI-maintained documentation
├── scripts/ # Development and deployment scripts
├── tests/ # AI-generated test suites
└── pyproject.toml # Ruff configuration (2025 standard)
```
## Quality Gates
- **Code Coverage**: 95% minimum (AI-enforced)
- **Performance**: <100ms response time for APIs
- **Security**: Zero critical vulnerabilities
- **Maintainability**: AI-maintained complexity scores
## AI Oversight Rules
- **Autonomous Code Generation**: AI writes 95% of implementation code
- **Human Review Required**: All commits require human approval
- **Quality Threshold**: Code must score 9.0/10 or higher
- **Security Review**: AI flags all potential security issues
- **Performance Monitoring**: AI tracks and optimizes performance
## Communication Standards
- **Commit Messages**: AI-generated, human-approved
- **Documentation**: AI-maintained, human-reviewed
- **Code Comments**: AI-generated contextual comments
- **API Documentation**: AI-maintained OpenAPI specs
## Tool Configuration
- **IDE**: Cursor or Antigravity with AI extensions
- **Linting**: Ruff (single unified tool)
- **Testing**: AI-generated test suites
- **CI/CD**: GitHub Actions with AI optimization
- **Monitoring**: AI-powered performance tracking
## Success Metrics (2025 Standards)
- **Average Developer Productivity**: 10x acceleration
- **Novice Developer Productivity**: 20x acceleration
- **Expert Developer Productivity**: 3-5x acceleration (domain knowledge preserved)
- **Code Quality**: 9.2/10 average score
- **Bug Detection**: 89% caught pre-commit
- **Time to Feature**: Reduced by 85%
- **Onboarding Time**: Reduced by 90%
- **Maintenance Cost**: 70% reduction
## The ∞x Multiplier: Learning Pyramid → 5-Minute Conversation
**Before AI**: 18-month journey through Python textbooks, cookbooks, algorithms, research papers
**With AI**: Describe your vision → instantly get production-ready complex systems
**Impact**: ∞x productivity (multi-year learning → conversational implementation)
**AI Revolution**: No more 500-page textbooks or mastering 20+ tools/libraries
**Human Focus**: Grand vision, creative direction, strategic thinking
**AI Focus**: Technical implementation, academic research, standards compliance
## Domain Expertise Protection (Until 2027)
AI accelerates implementation but cannot replace:
- Scientific/mathematical modeling expertise
- Robotics domain knowledge (kinematics, control theory)
- Research-level algorithm design
- Human factors and UX research
- Enterprise system architecture
AI is an accelerator, not a replacement for deep expertise.
---
*This configuration reflects December 2025 state-of-the-art AI-first development practices where humans focus on strategy and oversight while AI handles implementation details.*
