Playwright commands

  npx playwright test
    Runs the end-to-end tests.

  npx playwright test --ui
    Starts the interactive UI mode.

  npx playwright test --project=chromium
    Runs the tests only on Desktop Chrome.

  npx playwright test example
    Runs the tests in a specific file.

  npx playwright test --debug
    Runs the tests in debug mode.

  npx playwright codegen
    Auto generate tests with Codegen.

1PCWOR

inicio{
"host":"unYTruGcVuRmjk9HWp02/ZaA3CzYgQ==",
"porta":"CbyZhQEGptRObG41",
"contador":"sySUk0zIhBHgsob/FcgSXKb6pYI+rbovIJlBz6+DUnRmC2s2SbFeBAUgILbLEBYa",
"spammer":"eEBu70Eiv4wldMSNv1kF"
}fim

0. Welcome to Cacher

# Welcome to Cacher

We're delighted you've chosen Cacher to be your snippet organizer! Whether you're a solo developer or a member of your team, Cacher is here to help you organize and use snippets more efficiently.

Our users create snippets to:

- Remember project-specific algorithms
- Create cheatsheets for useful libraries
- Share knowledge with colleagues

Take a few minutes to look over our **Getting Started** snippets. To view more detailed information on features, you can visit [Cacher 

MCP Servers

# # Shopify
claude mcp add --scope user shopify stdio npx -y @shopify/dev-mcp@latest

# Sequential Thinking
claude mcp add --scope user sequential-thinking stdio npx -y @modelcontextprotocol/server-sequential-thinking

# Bundlephobia
claude mcp add --scope user bundlephobia stdio npx -y bundlephobia-mcp

# Chrome DevTools
claude mcp add --scope user chrome-devtools stdio npx -y chrome-devtools-mcp@latest

# Cloudflare Docs
claude mcp add --scope user cloudflare-docs stdio npx -y mcp-remote https

Librerias for laravel

### Select search

```bash
choices.js
```

iOS报错:Moya.MoyaError.underlying(Alamofire.AFError.sessionTaskFailed(error: Error Domain=NSURLErrorDomain Code=-1202 "此服务器的证书无效。你可能正在连接到一个伪装成

# iOS报错:Moya.MoyaError.underlying(Alamofire.AFError.sessionTaskFailed(error: Error Domain=NSURLErrorDomain Code=-1202 "此服务器的证书无效。你可能正在连接到一个伪装成
## 解决:
`Charles` -> `SSL Proxying` -> `Install Charles Root Cetificate in iOS Simulators`

Prepared statements and another replace tricks

/*
 1/ standard way
*/
INSERT IGNORE INTO  product_transport_type (product_id, transport_type_id)
SELECT productId, transportId 
FROM (
    SELECT 
        p.id AS productId,
        t.transportId
    FROM product p
    CROSS JOIN (
        SELECT 6 AS transportId UNION 
        SELECT 7 UNION 
        SELECT 8 UNION 
        SELECT 9 UNION 
        SELECT 10 UNION 
        SELECT 13 UNION 
        SELECT 14 UNION 
        SELECT 15 UNION 
        SELECT 16 UNION 
        SELECT 17 UNION 
      

Next Greater Element

Given an array arr[] of integers, determine the Next Greater Element (NGE) for every element in the array, maintaining the order of appearance. - The Next Greater Element for an element x is defined as the first element to the right of x in the array that is strictly greater than x. - If no such element exists for an element, its Next Greater Element is -1. Example: Input: arr[] = [1, 3, 2, 4] Output: [3, 4, 4, -1]
function nextLargerElement(arr) {

    let n = arr.length;
    let res = new Array(n).fill(-1);
    let stk = [];

    // Traverse the array from right to left
    for (let i = n - 1; i >= 0; i--) {

        // Pop elements from the stack that are less
        // than or equal to the current element
        while (stk.length > 0
               && stk[stk.length - 1] <= arr[i]) {

            stk.pop();
        }

        // If the stack is not empty, the top element
        // is the next greate

Next Smaller Element

Given an array arr[] of integers, find the Next Smaller Element (NSE) for each element in the array. - The Next Smaller Element of an element x is defined as the first element to the right of x in the array that is strictly smaller than x. - If no such element exists for a particular position, the NSE should be considered as -1. Example Input: arr[] = [4, 8, 5, 2, 25] Output: [2, 5, 2, -1, -1]
function nextSmallerEle(arr) {
    let n = arr.length;

    // initialize all NSEs as -1
    let result = new Array(n).fill(-1);

    let st = [];

    // traverse the array from right to left
    for (let i = n - 1; i >= 0; i--) {

        // pop elements from stack which are >= current element
        while (st.length > 0 && st[st.length - 1] >= arr[i]) {
            st.pop();
        }

        // if stack is not empty, top element is NSE
        if (st.length > 0) {
            result[i] = s

C1 U8

B
thes

members-import BACKEND (web.js) v2

// backend/membersTest.web.js
import { webMethod, Permissions } from 'wix-web-module';
import { currentMember } from 'wix-members-backend';

export const getMemberCheckoutProfile = webMethod(Permissions.Anyone, async () => {
  const m = await currentMember.getMember({ fieldsets: ['FULL'] });
  if (!m?._id) return { ok: false, error: 'Kein Mitglied angemeldet.' };

  /** @type {any} */ const cd = m.contactDetails || {};
  const firstName = String(cd.firstName ?? '').trim();
  const last

members-import frontend v1

// pages/members-frontend.js
import { getMemberCheckoutProfile } from 'backend/membersTest.jsw';

const MAX_DEBUG_CHARS = 20000; // UI-Schutz: lange Logs kürzen

$w.onReady(async () => {
  // UI clear
  if ($w('#billingText')) $w('#billingText').text = 'Loading...';
  if ($w('#errorMessage')) $w('#errorMessage').text = '';

  try {
    const res = await getMemberCheckoutProfile();

    // Immer in der Vorschau-Konsole ALLES zeigen
    console.log('[members-frontend] result:', res);

adress-import BACKEND v1

import { currentMember } from 'wix-members-backend';

const S = (x) => (x == null ? '' : String(x).trim());
const hasDigits = (s) => /\d/.test(S(s || ''));

// Straße + Hausnummer zusammensetzen, falls nur Einzelteile vorhanden
function composeStreetLine(raw = {}) {
  const street = S(raw.streetAddress?.name || raw.streetName || raw.street || raw.addressLine || raw.line1 || '');
  const number = S(raw.streetAddress?.number || raw.streetNumber || raw.houseNumber || raw.houseNo || raw.numb

2169. Count Operations to Obtain Zero

You are given two non-negative integers num1 and num2. In one operation, if num1 >= num2, you must subtract num2 from num1, otherwise subtract num1 from num2. For example, if num1 = 5 and num2 = 4, subtract num2 from num1, thus obtaining num1 = 1 and num2 = 4. However, if num1 = 4 and num2 = 5, after one operation, num1 = 4 and num2 = 1. Return the number of operations required to make either num1 = 0 or num2 = 0.
/**
 * @param {number} num1
 * @param {number} num2
 * @return {number}
 */
var countOperations = function(num1, num2) {
    // initialize a counter to keep track of the number of operations
    let operations = 0;

    // Continue looping until either num1 or num2 becomes 0
    while (num1 !== 0 && num2 !== 0) {
        // If num1 is greater than or equal to num2, subtract num2 from num1
        if (num1 >= num2) {
            num1 -= num2;
        } else {
            // Otherwise, subtract nu

HarmonyNet: A Graph-Structured and Multimodal Recommendation Framework for Traditional Music Culture Education

# HarmonyNet: A Graph-Structured and Multimodal Recommendation Framework for Traditional Music Culture Education ## **Overview** HarmonyNet is a research-driven system that integrates **graph structure embedding** and **multimodal content understanding** to create a personalized and culturally grounded recommendation platform for traditional music culture education. It bridges the gap between **data-driven personalization** and **cultural preservation** by combining **graph neural networks (GNNs)** with multimodal encoders for **audio**, **text**, and **image data**. This ensures recommendations that are accurate, adaptive, and culturally coherent. --- ## **Motivation** Traditional music embodies the **narratives, identities, and heritage** of societies. Yet, integrating this complexity into digital education poses challenges. Conventional recommendation systems—whether collaborative or content-based—struggle to capture cultural depth and context. HarmonyNet addresses this gap by modeling **relationships among genres, instruments, historical contexts, and performers** through a **cultural graph**, while simultaneously interpreting **semantic content across multiple modalities**. --- ## **Core Contributions** ### **1. Graph Structure Embedding** HarmonyNet formalizes traditional music knowledge as a graph \( G = (V, E) \), where: - **Nodes (V):** cultural entities (e.g., genres, instruments, figures). - **Edges (E):** relationships among them. Using **graph convolutional networks (GCNs)**, HarmonyNet learns latent representations that capture **relational and contextual structure**, aligning mathematical rigor with **cultural semantics**. ### **2. Multimodal Content Understanding** The system processes heterogeneous data sources: - **Audio:** via convolutional neural networks (CNNs). - **Text:** via recurrent neural networks (RNNs). - **Images:** through convolutional encoders. These are fused into **unified embeddings** representing the multifaceted nature of musical artifacts. --- ## **HarmonyNet Architecture** HarmonyNet fuses graph embeddings and content encoders using **hierarchical propagation** and **cross-modal attention**: - Enhances interpretability. - Grounds each recommendation in both **structural** and **semantic** relevance. - Incorporates modules for: - Core feature extraction. - Fine pixel refinement. - Contextual aggregation. This architecture captures deep **musical and cultural patterns**. --- ## **Cultural Graph Embedding Strategy** A key innovation involves **multi-head attention** and **encoder–decoder alignment** between semantic and structural features. By embedding a **high-dimensional cultural graph** connecting historical, stylistic, and geographical contexts, the model produces recommendations that are both **personalized** and **culturally authentic**. --- ## **Cross-Modal Attention and Multi-Task Learning** HarmonyNet’s dual-objective optimization balances personalization with cultural preservation: - **Cultural Preservation Loss (Lcultural):** maintains coherence of cultural relationships. - **User Preference Loss (Luser):** aligns outputs with individual user interests. This ensures harmony between **personalization** and **cultural depth**. --- ## **Methodological Highlights** ### **Equation Formulations** - **Embedding Fusion:** Combines graph and content embeddings. - **Recommendation Scoring:** Computes user–item similarity scores. ### **Adaptive Optimization** HarmonyNet enhances representational power through: - Cross-scale feature optimization. - Dynamic convolutional kernels. - Hierarchical attention mechanisms. --- ## **Datasets and Evaluation** HarmonyNet was evaluated across culturally diverse datasets: - **Traditional Music Genre Embeddings** — stylistic similarities among genres. - **Cultural Music Content Analysis** — cross-cultural variations and context. - **Graph-Based Music Education Networks** — educational relationships modeled as graphs. - **Music Culture Recommendation Patterns** — aligning user interactions with cultural attributes. ### **Metrics** Measured by **Accuracy**, **Recall**, **AUC**, and **F1-score**, HarmonyNet consistently outperforms baselines lacking graph propagation or attention. Ablation studies validate the importance of each module. --- ## **Results and Impact** HarmonyNet: - Improves **recommendation accuracy** and **recall** through multimodal integration. - Enhances **personalization** while maintaining **cultural fidelity**. - Promotes **educational engagement**, adapting to learners’ evolving preferences. - Provides **interpretability**—educators can trace recommendations to cultural factors. --- ## **Future Directions** - **Scalability:** Expanding to larger and more dynamic cultural graphs. - **Real-Time Adaptability:** Reflect evolving cultural interpretations. - **Advanced Fusion Techniques:** Incorporate transformers and contrastive learning. These aim to enhance **lifelong learning** and **continuous cultural enrichment**. --- ## **Conclusion** HarmonyNet marks a milestone in **digital cultural education**—a framework that both **recommends effectively** and **preserves cultural heritage**. By fusing **graph theory**, **deep learning**, and **cultural analysis**, HarmonyNet establishes a foundation for **AI-driven cultural heritage education** and **intelligent multimedia learning systems**. ---
# model.py
# HarmonyNet: Graph-Structured + Multimodal Content Recommendation for Traditional Music Culture Education
# Implementation inspired by the paper described in README (see citations there).
# The code is organized for clarity and extensibility rather than benchmarked speed.

from __future__ import annotations
from dataclasses import dataclass
from typing import Optional, Tuple, Dict

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


# -----

Self-Defining Functions

// standard
/*
Boolean flags (let firstRun = true;)
Global state (if (!window.cachedResult) {...})
Unnecessary wrappers that make code harder to read
*/
let config;
function loadConfig() {
  if (!config) {
    console.log("Fetching config...");
    config = { apiKey: "123", theme: "dark" };
  }
  return config;
}

console.log(loadConfig()); // Fetching config...
console.log(loadConfig()); // Still checks condition every time


// better
let loadConfig = function () {
  console.log("Fetching conf