.\new-fullstack-app.ps1 -AppName "MySaaS" -Description "Customer management platform"

.\new-fullstack-app.ps1 `
  -AppName "EnterpriseDashboard" `
  -IncludeAI `
  -IncludeMCP `
  -IncludeFileUpload `
  -IncludeVoice `
  -Include2FA `
  -IncludePWA `
  -IncludeMonitoring

EnterpriseDashboard/
├── frontend/              # React + TypeScript + Chakra UI
│   ├── src/components/   # Reusable UI components
│   ├── src/pages/        # Application pages
│   ├── Dockerfile        # Frontend container
│   └── nginx.conf        # Production web server
├── backend/              # FastAPI + PostgreSQL + Redis
│   ├── app/
│   │   ├── api/          # REST API endpoints
│   │   ├── core/         # Business logic
│   │   ├── models/       # Database models
│   │   └── services/     # External integrations
│   ├── mcp_server.py     # MCP server with CLI
│   └── Dockerfile        # Backend container
├── infrastructure/
│   └── monitoring/       # Prometheus, Grafana, Loki
├── scripts/              # Automation scripts
├── docs/                 # Generated documentation
└── docker-compose.yml    # Complete orchestration

// Technology Stack Selection Matrix
interface TechStack {
  frontend: 'React' | 'Vue' | 'Angular' | 'Svelte';
  backend: 'FastAPI' | 'Express' | 'NestJS' | 'Django';
  database: 'PostgreSQL' | 'MongoDB' | 'Redis' | 'SQLite';
  deployment: 'Docker' | 'Kubernetes' | 'Vercel' | 'Railway';
  ai: 'OpenAI' | 'Anthropic' | 'Ollama' | 'HuggingFace';
}

MVP Stage:
├── Basic CRUD operations
├── Simple authentication
└── Essential features only

Growth Stage:
├── Advanced features (AI, voice, files)
├── Performance optimization
├── Scalability improvements
└── Enhanced monitoring

Enterprise Stage:
├── Microservices architecture
├── Advanced security
├── Multi-region deployment
└── Enterprise integrations

# Backend AI service supporting multiple providers
class AIProviderManager:
    def __init__(self):
        self.providers = {
            'openai': OpenAIProvider(),
            'anthropic': AnthropicProvider(),
            'ollama': OllamaProvider(),
            'lmstudio': LMStudioProvider()
        }

    async def generate_response(self, prompt: str, provider: str = 'auto') -> str:
        if provider == 'auto':
            provider = self.select_best_provider(prompt)

        return await self.providers[provider].complete(prompt)

# Document processing and vector search
class RAGSystem:
    def __init__(self):
        self.vector_store = ChromaDB()
        self.embeddings = SentenceTransformer()

    async def add_documents(self, documents: List[str]):
        embeddings = self.embeddings.encode(documents)
        self.vector_store.add(embeddings, documents)

    async def query(self, question: str, top_k: int = 3) -> List[str]:
        question_embedding = self.embeddings.encode([question])[0]
        results = self.vector_store.search(question_embedding, top_k)
        return [doc for doc, _ in results]

// Frontend streaming implementation
async function streamAIResponse(prompt) {
  const response = await fetch('/api/chat/stream', {
    method: 'POST',
    body: JSON.stringify({ prompt }),
    headers: { 'Content-Type': 'application/json' }
  });

  const reader = response.body.getReader();
  const decoder = new TextDecoder();

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value);
    // Update UI with streaming content
    updateChatInterface(chunk);
  }
}

# Multi-stage build for optimization
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production

FROM node:18-alpine AS runner
WORKDIR /app
COPY --from=builder /app/node_modules ./node_modules
COPY . .

# Security hardening
RUN addgroup --system --gid 1001 nodejs
RUN adduser --system --uid 1001 nextjs
USER nextjs

EXPOSE 3000
CMD ["npm", "start"]

version: '3.8'
services:
  frontend:
    build: ./frontend
    ports:
      - "3000:3000"
    environment:
      - REACT_APP_API_URL=http://backend:8000

  backend:
    build: ./backend
    ports:
      - "8000:8000"
    environment:
      - DATABASE_URL=postgresql://user:pass@db:5432/app
    depends_on:
      - db

  db:
    image: postgres:15
    environment:
      POSTGRES_DB: app
      POSTGRES_USER: user
      POSTGRES_PASSWORD: pass
    volumes:
      - postgres_data:/var/lib/postgresql/data

volumes:
  postgres_data:

End-to-End Tests (E2E)
    ↓ 20% of tests
Integration Tests
    ↓ 30% of tests
Unit Tests
    ↓ 50% of tests

// Component testing with React Testing Library
import { render, screen, fireEvent } from '@testing-library/react';
import userEvent from '@testing-library/user-event';
import { ChatInterface } from './ChatInterface';

describe('ChatInterface', () => {
  it('displays user messages and AI responses', async () => {
    render(<ChatInterface />);

    const input = screen.getByRole('textbox');
    const submitButton = screen.getByRole('button', { name: /send/i });

    await userEvent.type(input, 'Hello AI');
    await userEvent.click(submitButton);

    expect(screen.getByText('Hello AI')).toBeInTheDocument();
    await waitFor(() => {
      expect(screen.getByText(/AI response/)).toBeInTheDocument();
    });
  });
});

# API testing with FastAPI TestClient
from fastapi.testclient import TestClient
from app.main import app

client = TestClient(app)

def test_create_user():
    response = client.post(
        "/users/",
        json={"name": "Test User", "email": "test@example.com"}
    )
    assert response.status_code == 201
    data = response.json()
    assert data["name"] == "Test User"
    assert "id" in data

def test_ai_chat_streaming():
    with client.websocket_connect("/ws/chat") as websocket:
        websocket.send_text("Hello AI")

        # Test streaming response
        response_chunks = []
        while True:
            data = websocket.receive_text()
            response_chunks.append(data)
            if "[END]" in data:
                break

        full_response = "".join(response_chunks)
        assert len(full_response) > 0

# .github/workflows/deploy.yml
name: Deploy to Production

on:
  push:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Setup Node.js
        uses: actions/setup-node@v4
        with:
          node-version: '18'
      - name: Install dependencies
        run: npm ci
      - name: Run tests
        run: npm test
      - name: Build
        run: npm run build

  deploy:
    needs: test
    runs-on: ubuntu-latest
    steps:
      - name: Deploy to production
        run: |
          docker-compose -f docker-compose.prod.yml up -d --build

{
  "version": 2,
  "builds": [
    {
      "src": "package.json",
      "use": "@vercel/next"
    }
  ],
  "routes": [
    {
      "src": "/api/(.*)",
      "dest": "/api/$1"
    },
    {
      "src": "/(.*)",
      "dest": "/$1"
    }
  ]
}

[build]
builder = "dockerfile"

[deploy]
healthcheckPath = "/health"
healthcheckTimeout = 300
restartPolicyType = "ON_FAILURE"
restartPolicyMaxRetries = 10

// Code splitting with React.lazy
const ChatInterface = lazy(() => import('./components/ChatInterface'));
const Analytics = lazy(() => import('./components/Analytics'));

// Image optimization
import { Image } from 'next/image';

export default function OptimizedImage({ src, alt }) {
  return (
    <Image
      src={src}
      alt={alt}
      width={800}
      height={600}
      placeholder="blur"
      blurDataURL="data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQ..."
      priority
    />
  );
}

# Async database operations
from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine

engine = create_async_engine(
    "postgresql+asyncpg://user:password@localhost/db",
    pool_size=10,
    max_overflow=20,
)

async def get_user(user_id: int) -> User:
    async with AsyncSession(engine) as session:
        result = await session.execute(
            select(User).where(User.id == user_id)
        )
        return result.scalar_one()

# Multi-level caching
from cachetools import TTLCache, LRUCache
from redis.asyncio import Redis

# In-memory cache for frequent requests
memory_cache = TTLCache(maxsize=1000, ttl=300)

# Redis for distributed caching
redis_cache = Redis(host='localhost', port=6379)

async def cached_api_call(endpoint: str, params: dict):
    cache_key = f"{endpoint}:{hash(str(params))}"

    # Check memory cache first
    if cache_key in memory_cache:
        return memory_cache[cache_key]

    # Check Redis cache
    redis_result = await redis_cache.get(cache_key)
    if redis_result:
        return json.loads(redis_result)

    # Make API call
    response = await make_api_call(endpoint, params)
    result = response.json()

    # Cache results
    memory_cache[cache_key] = result
    await redis_cache.set(cache_key, json.dumps(result), ex=3600)

    return result

# JWT-based authentication
from fastapi import Depends, HTTPException
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
import jwt

security = HTTPBearer()

async def get_current_user(credentials: HTTPAuthorizationCredentials = Depends(security)):
    try:
        payload = jwt.decode(credentials.credentials, SECRET_KEY, algorithms=["HS256"])
        user_id = payload.get("sub")
        if not user_id:
            raise HTTPException(status_code=401, detail="Invalid token")
        return await get_user_by_id(user_id)
    except jwt.ExpiredSignatureError:
        raise HTTPException(status_code=401, detail="Token expired")
    except jwt.InvalidTokenError:
        raise HTTPException(status_code=401, detail="Invalid token")

from pydantic import BaseModel, Field, validator
from typing import Optional
import bleach

class UserInput(BaseModel):
    name: str = Field(..., min_length=1, max_length=100)
    email: str = Field(..., regex=r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$')
    bio: Optional[str] = Field(None, max_length=500)

    @validator('bio')
    def sanitize_bio(cls, v):
        if v:
            # Sanitize HTML input
            return bleach.clean(v, tags=[], strip=True)
        return v