alonsoir

import moderngl
import numpy as np
import glfw

# Inicializar GLFW
if not glfw.init():
    raise Exception("No se pudo inicializar GLFW")

# Establecer la versión de OpenGL que queremos usar (por ejemplo, OpenGL 3.3)
glfw.window_hint(glfw.CONTEXT_VERSION_MAJOR, 3)

alonsoir

# falco_rules.yaml

- list: authorized_devices
  items: [device_1, device_2, device_3]

- list: malicious_urls
  items: [malicious.com, phishing.com, malware.com]

- rule: High Frequency QR Scans
  desc: Detect multiple scans of the same QR code in a short time.

alonsoir

from transformers import BartForConditionalGeneration, BartTokenizer
import requests
from bs4 import BeautifulSoup

## Function to summarize article
def summarize_article(article_text, max_length=150):
    model_name = "facebook/bart-large-cnn"
    tokenizer = BartTokenizer.from_pretrained(model_name)
    model = BartForConditionalGeneration.from_pretrained(model_name)

alonsoir

import streamlit as st

# NATO phonetic alphabet encoder dictionary
nato_encoder = {
    'A': 'Beta', 'B': 'Gamma', 'C': 'Cypher', 'D': 'Epsilon',
    'E': 'Zeta', 'F': 'Eta', 'G': 'Theta', 'H': 'Iota',
    'I': 'Kappa', 'J': 'Lambda', 'K': 'Mu', 'L': 'Nu',
    'M': 'Xi', 'N': 'Omicron', 'O': 'Pi', 'P': 'Rho',
    'Q': 'Sigma', 'R': 'Tau', 'S': 'Upsilon', 'T': 'Phi',
    'U': 'Chi', 'V': 'Psi', 'W': 'Omega', 'X': 'Alpha',

alonsoir

import pandas as pd
from faker import Faker
import random

fake = Faker()

def generate_fake_data(num_entries=10):
    data = []

    for _ in range(num_entries):

alonsoir

---
Query original:

SELECT
u.user_id,u.username,u.email, COALESCE(SUM(o.total_amount), 0) AS total_ spent, COUNT(DISTINCT o. order_id) AS total_orders,
ROUND (AVG(o. total_amount, 2) AS avg_order_value,
MAXo. total amount) AS max order_value,
RANK() OVER (ORDER BY SUM(o. total_amount) DESC) AS spending_rank,
SELECT MAX(02. order_date)
FROM orders 02

alonsoir

#!/bin/bash

# Prompt the user for a link
echo "Please enter the link to scan:"
read link

# Output the entered link
echo "You entered: $link"

curl -s "https://crt.sh/?q=%25.$link&output=json" | jq -r '.[].name_value' | sed 's/\*\.//g' | sort -u

alonsoir

from openai import OpenAI
import requests
from dotenv import load_dotenv
import time
import os
import json

load_dotenv()
client = OpenAI()

alonsoir

# pip install openai
# para probarlo, me bajo el libro Moby Dick, lo divido en 72 secciones y envio cada seccion gpt-4.
# Hay que tener cuidado con los parametros MAX_TOKENS_PER_CHUNK y SUMMARY_TOKEN_LIMIT para no molestar a openAI.
from openai import OpenAI
import requests
from dotenv import load_dotenv

load_dotenv()
client = OpenAI()

alonsoir

public class ListaDobleEnlazada<T> {
    private Nodo<T> cabeza;
    private Nodo<T> cola;

    // Clase interna Nodo
    private static class Nodo<T> {
        T dato;
        Nodo<T> siguiente;
        Nodo<T> anterior;