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// iOS App Architecture
import UIKit
import SwiftUI
import Combine

class AppCoordinator {
    private let window: UIWindow
    private var cancellables = Set<AnyCancellable>()

    func start() {
        let viewModel = MainViewModel()
        let mainView = MainView(viewModel: viewModel)

        window.rootViewController = UIHostingController(
            rootView: mainView
        )
        window.makeKeyAndVisible()

        // Handle deep linking
        NotificationCenter.default
            .publisher(for: .handleDeepLink)
            .sink { [weak self] notification in
                self?.handleDeepLink(notification.object as? URL)
            }
            .store(in: &cancellables)
    }
}

// RESTful API with Express.js
const express = require('express');
const router = express.Router();
const { authenticate, rateLimit } = require('../middleware');

router.post('/api/users',
    authenticate,
    rateLimit({ max: 100, window: '15m' }),
    async (req, res) => {
        try {
            const user = await User.create(req.body);
            const token = jwt.sign({ id: user.id }, process.env.JWT_SECRET);

            res.status(201).json({
                success: true,
                data: { user, token }
            });
        } catch (error) {
            res.status(400).json({ error: error.message });
        }
    }
);

module.exports = router;

# Machine Learning Model
import tensorflow as tf
from tensorflow import keras
import numpy as np

class ModelTrainer:
    def __init__(self, input_shape, num_classes):
        self.model = self.build_model(input_shape, num_classes)

    def build_model(self, input_shape, num_classes):
        model = keras.Sequential([
            keras.layers.Dense(128, activation='relu', input_shape=input_shape),
            keras.layers.Dropout(0.3),
            keras.layers.Dense(64, activation='relu'),
            keras.layers.Dense(num_classes, activation='softmax')
        ])

        model.compile(
            optimizer='adam',
            loss='categorical_crossentropy',
            metrics=['accuracy']
        )
        return model

    def train(self, X_train, y_train, epochs=50):
        return self.model.fit(X_train, y_train, epochs=epochs)