Cacher is the code snippet organizer for pro developers

[Subject]: A complex, vintage Chicano tattoo-style graphic composition featuring a large menacing skull wearing aviator sunglasses and a snapback cap reading 'Nawf Side'. Below the skull is elaborate, ornate gothic typography reading 'TRILLA THAN THA REST' intertwined with decorative flowing ribbons. At the base sits a classic 90s luxury lowrider sedan with wire spoke wheels, flanked by stacks of hundred dollar bills, a smoking double styrofoam cup, a giant faceted diamond, and a smaller skull r

You are a veteran Full-Stack Web Developer, Certified Google Cloud Engineer, and AI Engineer. Your tool stack is Javascript-based, with Node.JS for the backend and for Machine Learning algorithms, and React Native, Next.js and other js frameworks for styling. You are my mentor in Google Developer Program, and Google Cloud. You will be guiding and tutoring me in Full-Stack Cloud Dev, AI Engineering, Prompt Engineering, and freelance work.

# バックエンドエンジニアリング基礎領域マップ

実務で着手する順に構成。

---

## 1. Linuxとシェルの基礎

すべての土台。クラウド環境の問題も、実態はLinuxレベルの問題であることが多い。

- **操作**：ファイルシステムのナビゲーション / パーミッション管理（chmod / chown） / ポート管理（lsof / ss） / 環境変数
- **プロセス管理**：ps / kill / バックグラウンド実行（nohup / &） / ゾンビプロセスの検出と対処
- **サービス管理**：systemdによるサービスの起動・停止・自動再起動設定（systemctl enable / start / status）
- **リソース監視**：
  - **CPU**：top / htop / mpstat
  - **メモリ**：free / vmstat
  - **ディスク**：df / du / iostat
  - **ネットワーク**：iftop / nethogs
- **ログ調査**：grep / tail -f / less / journa

# バックエンドエンジニアリング基礎領域マップ

実務で着手する順に構成。

---

## 1. Linuxとシェルの基礎

すべての土台。クラウド環境の問題も、実態はLinuxレベルの問題であることが多い。

- **操作**：ファイルシステムのナビゲーション / パーミッション管理（chmod / chown） / ポート管理（lsof / ss） / 環境変数
- **プロセス管理**：ps / kill / バックグラウンド実行（nohup / &） / ゾンビプロセスの検出と対処
- **サービス管理**：systemdによるサービスの起動・停止・自動再起動設定（systemctl enable / start / status）
- **リソース監視**：
  - **CPU**：top / htop / mpstat
  - **メモリ**：free / vmstat
  - **ディスク**：df / du / iostat
  - **ネットワーク**：iftop / nethogs
- **ログ調査**：grep / tail -f / less / journa

<!-- ==========================================================
  Speculation Rules API テンプレート
  構成: prefetch + prerender + 副作用URL除外 + セレクター除外
  ※ type="speculationrules" の中身はJSONとしてパースされるためコメントを含められない。HTML側で補足する
  ※ 配置: head内またはbody内。JSとして実行されないため他のscriptとの順序は無関係
  ※ 無効化条件:
      Save-Dataヘッダー有効時
      省エネモード かつ バッテリー残量低下時
      メモリ不足時
========================================================== -->

<!-- prefetch with eager: HTMLのみ取得。発動タイミングはブラウザ依存で、immediateとmoderateの間 -->
<!-- 同時保持上限は2件。上限到達時は

/**
 * @param {number[]} nums1
 * @param {number[]} nums2
 * @return {number}
 */
var maxDistance = function(nums1, nums2) {
    let i = 0, j = 0;
    let ans = 0;

    while (i < nums1.length && j < nums2.length) {
        if (nums1[i] <= nums2[j]) {
            ans = Math.max(ans, j - i);
            j++; // try to go farther
        } else {
            i++; // need a smaller nums1[i]
        }
    }

    return ans;
};

# GCP Useful Commands

## CLI

### Buckets

* List all files: `gcloud storage ls gs://your-bucket-name`

# PYTHON & PATH

## PATH "HOT LOAD"
Sometimes we just want to add a module / library for a specific project, and "hot load" it.
We can modify the search path for this project by running the following command ( before we acutally load the module)

Rather than a global mod to system env PYTHONPATH, we use the following code; ( here for inside of touchdesigner )

Firing an Execute DAT onStart() with the code snippet below:


### example execute dat in TD.
```
import sys
mypath = "C:/Python311/Lib/s

/**
 * @param {number} n
 * @return {number}
 */
var mirrorDistance = function(n) {
    let original = n;
    let rev = 0;

    // Reverse the digits of n
    while (n > 0) {
        rev = rev * 10 + (n % 10);
        n = Math.floor(n / 10);
    }

    // Mirror distance = |n - reverse(n)|
    return Math.abs(original - rev);
};

/**
 * @param {number[]} nums
 * @return {number}
 */
var minMirrorPairDistance = function(nums) {
    const last = new Map();     // value -> most recent index of reverse(nums[i])
    let ans = Infinity;

    function rev(x) {
        let r = 0;
        while (x > 0) {
            r = r * 10 + (x % 10);
            x = Math.floor(x / 10);
        }
        return r;
    }

    for (let i = 0; i < nums.length; i++) {
        const x = nums[i];

        // If we've seen a reversed partner before,

# Command line instructions

You can also upload existing files from your computer using the instructions below.

## Configure your Git identity

Get started with Git and learn how to configure it.

## Git local setup

Configure your Git identity locally to use it only for this project:

```
git config --local user.name "Xuan NGUYEN"
git config --local user.email "xuxu.fr@gmail.com"
```

## Add files

Push files to this repository using SSH or HTTPS. If you're unsure, we recom

tight deadline
Make amendments = make changes
from around the world
buyer's remorse
work laptop

H
man made forest
avocado stones
electronic waste
lanfill sides
leftover food
lower-income
famine

I
doom and gloom
heading from
baked by
taking off
killing two birds with one stone
drive down

J
grasroot changes
Lunartic
Lunar
to imbridge
at work/ at work place

/**
 * @param {number[]} nums
 * @param {number[]} queries
 * @return {number[]}
 */
var solveQueries = function(nums, queries) {
    const n = nums.length;
    const last = new Map();
    const dp = new Array(n).fill(n); // n = "no match yet"

    // Walk through array twice to simulate circular behavior
    for (let i = 0; i < 2 * n; i++) {
        const idx = i % n;
        const num = nums[idx];

        if (last.has(num)) {
            const prev = last.get(num);
            const dist = i 

Pandas support page: http://pandas.pydata.org/

Acess a specific column
  df["column"]
  
Drop missing values
  df.dropna(subset=["column"], axis=0, inplace = True)
  
Replave missing values
  df["missing_value"].replace(np.nan, new_value)
    ex.mean_of_table = df["loses"].mean()
      df["loses"].replace(np.nan, mean)

Identify types
  df.dtype()
  
Convert one dataframe to another type
  df.astype()
    ex. df["price"] = df["price"].astype("int")

Normailze datatypes
  Simple  feature scaling

/**
 * @param {string[]} words
 * @param {string} target
 * @param {number} startIndex
 * @return {number}
 */
var closestTarget = function(words, target, startIndex) {
    const n = words.length;

    // We'll track the smallest circular distance found.
    // Start with Infinity so any real distance will be smaller.
    let best = Infinity;

    // Check every index in the array.
    for (let i = 0; i < n; i++) {

        // Only consider positions where the word matches the target.
        if