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

Img Prompt 

# NANO BANANA
#### EDITING ( reconstruction guide )
`Remove the people in the background while keeping the main subject in the foreground untouched. Reconstruct the background naturally where the background people were removed. Maintain realistic lighting and scene integrity.`

To Check {Syntax} ? 

# PROMPT GUIDES

### Prompt spreadsheets / cheat list
[link to gSheet "Prompting Workflow
"](https://docs.google.com/spreadsheets/d/1yqhKY8q3eY3nZl9fgf1sQMHRnQENGFHmm2FamfxKhIw/edit?gid=0#gid=0)\
thi

reusable test route for supabase 

'use client'
import { useEffect, useState } from 'react'
import { supabase } from '@/lib/supabase'
export default function SupabaseConnectionTest() {
  // 🧠 This will stop the page from running in production
  if (process.env.NODE_ENV === 'production') {
    return (
      <div style={{ padding: '2rem', fontFamily: 'system-ui', textAlign: 'center' }}>
        <h1>🛑 Supabase Connection Test</h1>
        <p>This page is disabled in production.</p>
      </div>
    )
  }
  const [connectionStatus,

Extracting Multiple Tokens from Text File 

English   John      Paul      George    Richard
Spanish   Juan      Pablo     Jorge     Ricardo
French    Jean      Pol       Georges   Richard
Italian   Giovanni  Paolo     Giorgio   Riccardo

1st snippet

Description of the snippet
echo Welcome World

1611. Minimum One Bit Operations to Make Integers Zero

Given an integer n, you must transform it into 0 using the following operations any number of times: Change the rightmost (0th) bit in the binary representation of n. Change the ith bit in the binary representation of n if the (i-1)th bit is set to 1 and the (i-2)th through 0th bits are set to 0. Return the minimum number of operations to transform n into 0.
/**
 * Calculates the minimum number of operations to reduce a number `n` to 0
 * using a specific bitwise operation rule:
 * - You can flip the lowest 1-bit and all bits to its right.
 *
 * This function uses a recursive approach based on the properties of Gray code.
 *
 * @param {number} n - The input number.
 * @return {number} - Minimum number of operations to reduce `n` to 0.
 */
var minimumOneBitOperations = function(n) {
    // Base case: if n is 0 or 1, return n directly
    if (n <= 1)

forとsomeの使い分け 

// forループとsomeメソッドの違いを確認するサンプルコード

const numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];

// forループの例
// - 全要素を処理可能（breakで中断も可）
// - インデックスにアクセス可能
// - 返り値なし（自分で変数管理）
// - より柔軟な処理が可能
let foundValue = null;
let foundIndex = -1;

for (let i = 0; i < numbers.length; i++) {
    if (numbers[i] > 7) {
        foundValue = numbers[i];
        foundIndex = i;
        break; // 見つかったら中断
    }
}

console.log(`forループ結果: 値=${foundValue}, インデックス=${foundIndex}`);

// someメソッドの例
// - 条件を満たす要素が1つでもあればtrueを返す
// - 見つ

CSSだけでviewportの実数値pxを算出する 

/* viewportの横幅と高さの実数値を取得する */

@property --viewport-w {
  syntax: "<length>";
  inherits: true;
  initial-value: 100vw;
}
@property --viewport-h {
  syntax: "<length>";
  inherits: true;
  initial-value: 100vh;
}

body {
  --calculated-viewport-w: tan(atan2(var(--viewport-w), 1px));
  --calculated-viewport-h: tan(atan2(var(--viewport-h), 1px));
  background-color: orange;
}
body:before {
  content: counter(w) "x" counter(h);
  counter-reset: h var(--calculated-viewport-h) w var(--calculated-view

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iOS编译报错：Sandbox: bash(42595) deny(1) file-write-create 

# 编译报错：Sandbox: bash(42595) deny(1) file-write-create /Users/reedwen/Documents/demo/RxMVVMTableViewDemo/Pods/resources-to-copy-RxMVVMTableViewDemo.txt

## 解决：
选择指定target -> `Build Settings` -> 搜索`User Script Sandboxing`设置为`NO`

Python: PyQt5 + pynput 出现 illegal hardware instruction 问题分析与解决方案 

# 🧠 PyQt5 + pynput 出现 `illegal hardware instruction` 问题分析与解决方案

## 一、问题原因分析

1. **pynput 使用 Quartz API**
   - 在 macOS 下，`pynput.keyboard.Listener` 基于 Quartz 的 C 接口实现。
   - 而 PyQt5 的事件循环使用 Cocoa/Carbon 框架。
   - 两者若在同一线程运行，会发生 **信号冲突** 或 **事件循环竞争**，从而导致：
     ```
     illegal hardware instruction
     ```

2. **线程模型错误**
   - 若在 **Qt 主线程** 内启动 `pynput.Listener()`，会阻塞或破坏 Qt 的事件循环。
   - ✅ **正确做法：** 将 `pynput` 放在独立线程中运行。

3. **PyInstaller 打包后更容易触发**
   - 打包后的运行环境精简，某些动态库（如 Quartz）可能加载失败。
   - 特别是 `--o