Cacher is the code snippet organizer for pro developers

# windows oh my zsh

https://dev.to/pavlosisaris/windows-command-line-revolution-unleash-zsh-and-oh-my-zsh-a-simple-guide-for-developers-271o

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Y

sk-elh3acQm4tIUa8SbvkDoP1L5MSWBt4tdDDyokJTOwuDtYIZl

sk-elh3acQm4tIUa8SbvkDoP1L5MSWBt4tdDDyokJTOwuDtYIZl

/**
 * @param {number[][]} squares
 * @return {number}
 */
var separateSquares = function (squares) {
    // ------------------------------------------------------------
    // STEP 1: Compute total area (counting overlaps multiple times)
    // ------------------------------------------------------------
    let totalArea = 0;
    for (const [x, y, l] of squares) {
        totalArea += l * l;
    }

    const target = totalArea / 2; // We want areaBelow(h) = target

    // ---------------------

  # drop specific errors entirely
  config.before_send = lambda do |event, hint|
    exception = hint[:exception]

    if exception.is_a?(SpecificErrorToSample)
      # Sample SpecificErrorToSample at 10%
      return nil if rand < 0.1
    end

    # Return the event to send to Sentry
    event
  end

brand guidelines and tokens for paul weezy design, a digital design/development agency that has roots in streethwear, graffiti, houston, slab culture and influences from west coas lowriders, as well as being in the tattoo business for well over a decade, but knowing we were destined for more Paul Weezy Design eventually went to design school and was influenced by massimo vignilli, paula schere, and the likes of james victori, fell inlove with modernism and grids, but can reachin and look to his 

brand guidelines and tokens for paul weezy design, a digital design/development agency that has roots in streethwear, graffiti, houston, slab culture and influences from west coas lowriders, as well as being in the tattoo business for well over a decade, but knowing we were destined for more Paul Weezy Design eventually went to design school and was influenced by massimo vignilli, paula schere, and the likes of james victori, fell inlove with modernism and grids, but can reachin and look to his 

@echo off
setlocal enabledelayedexpansion

echo ============================================
echo  Video Migration + Symlink Tool
echo ============================================
echo.

:: Ask for local folder
set /p LOCAL_DIR=Paste FULL LOCAL folder path (e.g. L:\...\Videos): 

if not exist "%LOCAL_DIR%" (
    echo.
    echo ERROR: Local folder does not exist.
    pause
    exit /b
)

echo.
echo Scanning for video files in:
echo   %LOCAL_DIR%
echo.

pushd "%LOCAL_DIR%" |

button.btn.btn-outline-secondary.map-switch.fs-14.fw-500.lh-20.text-gray-800.rounded-pill
Specificity: (0,8,1)
 {
    background: white !important;
    width: 135.3px !important;
}

<?
require( $_SERVER[ "DOCUMENT_ROOT" ] . "/bitrix/header.php" );
//$deleteFiles = ‘yes’;
//$saveBackup = ‘yes’;
global $USER;
if ( !$USER->IsAdmin() ) {
  echo "Одумайся или авторизуйся...";
  return;
}
$time_start = microtime( true );
echo '
';
define( "NO_KEEP_STATISTIC", true );
define( "NOT_CHECK_PERMISSIONS", true );
$deleteFiles = 'yes'; //Удалять ли найденые файлы yes/no
$saveBackup = 'yes'; //Создаст бэкап файла yes/no
//Папка для бэкапа
$patchBackup = $_SERVER[ 'DOCUMENT_ROOT' ] . "/up

/**
 * @param {number[][]} points
 * @return {number}
 */
var minTimeToVisitAllPoints = function(points) {
    // Total time accumulator
    let totalTime = 0;

    // Iterate through each consecutive pair of points
    for (let i = 1; i < points.length; i++) {
        const [x1, y1] = points[i - 1];
        const [x2, y2] = points[i];

        // Compute horizontal and vertical distances
        const dx = Math.abs(x2 - x1);
        const dy = Math.abs(y2 - y1);

        // The key insight:
   

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from typing import Dict, List
import pandas as pd
from sklearn.base import BaseEstimator
from sklearn.metrics import accuracy_score, f1_score

def evaluate_model(
    model: BaseEstimator,
    X_test: pd.DataFrame,
    y_test: pd.Series,
    metrics: List[str] = ["accuracy", "f1"]
) -> Dict[str, float]:
    """
    Evalúa un modelo de Machine Learning sobre un conjunto de prueba.

    Args:
        model (BaseEstimator): Modelo entrenado de scikit-learn.
        X_test (pd.DataFrame): Features d

inicio{
"host":"AK3YRLeLHqw25E0FHbdP5wNR94aH",
"portacmd":"AaEq2UGu9RWuJP1J",
"portaimg":"LszyUxibss3K2g==",
"pronto":"RlUKy4l8k2P+NI0=",
"ipv":"T7fcOpUzbaUY",
"contador":"HQ0rqGWAUTXGhFazDVtRu0BemqJ7G+ciNwUt4a/87kMGvt/efUQrVyUWyZm6vSTgmhw=",
"chaveid":"Gx52gh0LCfV/j9Cb",
"spammer":"wtcieGrF1vx6A38="
}fim