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### Eliminar un dominio

Eliminar el enlace de activación
```
sudo rm /etc/nginx/sites-enabled/dominio.com
```

Eliminar el archivo de configuración:
```
sudo rm /etc/nginx/sites-available/dominio.com
```

Aplicar cambios y reiniciar Nginx
```
sudo nginx -t
sudo systemctl restart nginx
```

Borrar la Carpeta de Archivos
```
sudo rm -rf /var/www/dominio.com
```

```
./wp_setup.sh midominio.com ClaveFuerteYSegura!1
```

1. HR & Talent Acquisition (IT-focused)
HR Manager IT

Technical Recruiter

IT Recruiter

Talent Acquisition Specialist IT

HR Business Partner Technology

People Operations IT

Campus Recruiter IT

HR Generalist IT

2. IT Domain (General)
IT Specialist

IT Consultant

IT Project Manager

IT Support Engineer

IT Infrastructure Manager

Systems Administrator

Cloud Engineer

IT Operations Lead

3. Machine Learning / Deep Learning
Machine Learning Engineer

Deep Learning Engineer

AI Research Scie

/**
 * @param {number[]} nums
 * @return {number}
 */
var maxSumDivThree = function(nums) {
    // Step 1: Calculate the total sum of the array
    let totalSum = nums.reduce((acc, num) => acc + num, 0);

    // Step 2: If already divisible by 3, return it
    if (totalSum % 3 === 0) return totalSum;

    // Step 3: Separate numbers by their remainder when divided by 3
    let remainder1 = []; // numbers where num % 3 === 1
    let remainder2 = []; // numbers where num % 3 === 2

    for (let nu

# LangGraph Complete Study Notes

## 1. Core Concepts

### What is LangGraph?
- **Stateful graph framework** for building complex LLM workflows
- **Cyclic graphs** - supports loops and conditional flows
- **Built on LangChain** - integrates seamlessly with LCEL
- **Persistence** - save and resume workflows at any point

### Graph Types
- **StateGraph** - Main graph with typed state (most common)
- **MessageGraph** - Specialized for chat messages
- **WorkflowGraph** - Simple workflow

# LangChain
llms_chat_model, prompts,output_parsers, memory, chains, messages.py", rag_retrievers,
embedding, retrievers, runnables, callbacks, text_splitter, tools_toolkits, document_loader,
streaming.py", mcp_client,
        
# LangGraph

state, nodes, edges, checkpointer", persistence", workflows


# Langchain all important technique names

# LangChain Essential Techniques Reference

## 1. Prompts (Core Techniques)

### Must-Know
- **ChatPromptTemplate** - Primary template fo

# Notes

- Combine chat history and notes into a single markdown file.
- Note: Do not include unnecessary objects.
- Revise the notes file for a short, clear read.
- Correct only the prompt wording.

## List client

ls /etc/openvpn/easy-rsa/pki/issued/

## Delete client

su

cd /etc/openvpn/easy-rsa/

./easyrsa revoke client_to_delete

./easyrsa gen-crl

cp /etc/openvpn/easy-rsa/pki/crl.pem /etc/openvpn/

chmod 644 /etc/openvpn/crl.pem

systemctl restart openvpn

openssl crl -in /etc/openvpn/crl.pem -noout -text | grep -A1 "Revoked Certificates"

## Add client

./ubuntu-22.04-lts-vpn-server.sh

## Check certif expired 

openssl x509 -in /etc/openvpn/easy-rsa/pki/issued/server_*.crt -text -no

E STUDY THE RULES AND PUT THE DIRECT SPEECH INTO REPORTED SPEECH STARTING WITH "SHE SAID..."  REPORTED SPEECH  
1. "I’ve had better results than any other student on the program."  
She said, she had had better results...
2. "I saw you stealing money from the charity box and I’m going to report it right now."  
She said, she had seen you stealing money and she was going to report it right then
3. "We should do a sponsored run to raise money tomorrow."  
She said, we should do a sponsored run to 

def generate_correlation_report(df: pd.DataFrame, output_path: str = None) -> dict:
    """
    Generates a correlation metrics report:
    1. Strong correlations (>|0.80|)
    2. Very weak correlations (< 0.01)

    Optionally saves the report as JSON.

    Parameters
    ----------
    df : pd.DataFrame
        Input dataframe.
    output_path : str, optional
        File path to save JSON report.

    Returns
    -------
    dict
        Correlation summary report.
    """

import json
import os
from typing import Any, Dict
from datetime import datetime

class SaveJsonException(Exception):
    """Custom exception for JSON saving errors."""
    pass


def save_json(data: Dict[str, Any], file_path: str) -> None:
    """
    Save dictionary data to a JSON file with full reliability.
    
    Key Capabilities:
    -----------------
    • Creates directories automatically  
    • Supports UTF-8 encoding  
    • Atomic write operation (prevents corrupti

W3SCHOOLS
https://www.w3schools.com/bootstrap5/index.php

• Grids
https://www.w3schools.com/bootstrap5/bootstrap_grid_basic.php

• https://github.com/jqueryscript/Bootstrap-5-Design-Resources

Some of the best free options
• Lightbox for Bootstrap 5 (a.k.a. bs5-lightbox): This is a pure JavaScript lightbox gallery plugin specifically designed for Bootstrap 5. It leverages Bootstrap's Modal and Carousel components, ensuring a consistent look and feel with your existing Bootstrap 5 project. Listed in Bootstrap 5 Design Resources (see below).
• Chocolat: A free, lightweight, and responsive lightbox plugin that offers flexibility in how images are displayed (fullscreen or within a contain

/**
 * @param {number[]} nums
 * @return {number}
 */
var minimumOperations = function(nums) {
    // Initialize a counter to keep track of total operations
    let operations = 0;

    // Loop through each number in the array
    for (let num of nums) {
        // Find the remainder when divided by 3
        let remainder = num % 3;

        // If remainder is 1, subtract 1 -> costs 1 operation
        // If remainder is 2, add 1 -> costs 1 operation
        // If remainder is 0, no operation n

import torch
import torch.nn as nn
import torch.nn.functional as F


# --------------------------------------------------------------------
# 1. Syntactic Mapping Layer
# --------------------------------------------------------------------

class SyntacticMapping(nn.Module):
    """
    Implements:
    - syntactic relation matrix S (eq.4)
    - adjacency g(i,j) (eq.5)
    - syntactic similarity (eq.6)
    - word-level syntactic embedding (eq.7)
    """

    def __init__(self, e

import torch
import torch.nn as nn
import torch.nn.functional as F


# ---------------------------------------------------------
# Basic Modules: MLP, Attention, Transformer blocks
# ---------------------------------------------------------

class MLP(nn.Module):
    def __init__(self, dim, hidden):
        super().__init__()
        self.fc1 = nn.Linear(dim, hidden)
        self.fc2 = nn.Linear(hidden, dim)
        self.act = nn.GELU()

    def forward(self, x):
        return