Learn.Build.Master AI|

import numpy as np

def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# Simple 2-layer neural network
class NeuralNetwork:
    def __init__(self, input_size, hidden, output):
        self.W1 = np.random.randn(input_size, hidden)
        self.W2 = np.random.randn(hidden, output)
    
    def forward(self, X):
        self.z1 = np.dot(X, self.W1)
        self.a1 = sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.W2)
        return sigmoid(self.z2)

class TreeNode {
  constructor(val) {
    this.val = val;
    this.left = null;
    this.right = null;
  }
}

class BST {
  insert(root, val) {
    if (!root) return new TreeNode(val);
    if (val < root.val)
      root.left = this.insert(root.left, val);
    else
      root.right = this.insert(root.right, val);
    return root;
  }
  
  inorder(root, result = []) {
    if (!root) return result;
    this.inorder(root.left, result);
    result.push(root.val);
    this.inorder(root.right, result);
    return result;
  }
}

# Dockerfile for a Node.js app
FROM node:18-alpine

WORKDIR /app

COPY package*.json ./
RUN npm ci --only=production

COPY . .

EXPOSE 3000

CMD ["node", "server.js"]

# Build and run
# docker build -t myapp:v1 .
# docker run -p 3000:3000 myapp:v1

# Packet capture with Python scapy
from scapy.all import sniff, IP, TCP

def analyze_packet(pkt):
    if IP in pkt:
        src = pkt[IP].src
        dst = pkt[IP].dst
        proto = pkt[IP].proto
        
        if TCP in pkt:
            sport = pkt[TCP].sport
            dport = pkt[TCP].dport
            flags = pkt[TCP].flags
            print(f"{src}:{sport} → {dst}:{dport} [{flags}]")

# Capture 10 packets
sniff(count=10, prn=analyze_packet)