JohnPhamous

export const chaosTestStrings = (): void => {
  const textNodes = getAllTextNodes(document.body);

  for (const node of textNodes) {
    const textNodeLength = node.textContent ? node.textContent.length : 0;
    if (node.textContent === null) {
      return;
    }
    if (node.parentElement instanceof Element) {
      if (node.parentElement.dataset.originalText === undefined) {

soupaulogomes

[
  {
    "gameTime": "2022-11-20T16:00:00Z",
    "homeTeam": "cat",
    "awayTeam": "equ"
  },
  {
    "gameTime": "2022-11-21T13:00:00Z",
    "homeTeam": "ing",
    "awayTeam": "ira"

F9y4ng

// ==UserScript==
// @name               字体渲染（自用脚本）
// @name:zh-CN         字体渲染（自用脚本）
// @name:zh-TW         字型渲染（自用程式碼）
// @name:en            Font Rendering (Customized)
// @name:ja            フォントレンダリング (カスタム)
// @name:ko            폰트 렌더링 (개인용 스크립트)
// @version            2026.07.11.2
// @author             F9y4ng
// @description        无需 MacType，享受细腻高质感的网页阅读体验。脚本默认采用“微软雅黑”，支持自定义替换。面向进阶排版需求，集成字体重写、抗锯齿平滑、动态缩放、描边阴影、特殊样式过滤（白名单）及自定义等宽字体等高级功能。完美支持“全局渲染”与“站点个性化”双模式，点击图标或快捷键即可唤出配置面板。全面兼容主流浏览器、脚本管理器及常用扩展。

madebyollin

# ------------------------------------------------------------------
# EDIT: I eventually found a faster way to run SD on macOS, via MPSGraph (~0.8s / step on M1 Pro):
#   https://github.com/madebyollin/maple-diffusion
# The original CoreML-related code & discussion is preserved below :)
# ------------------------------------------------------------------

# you too can run stable diffusion on the apple silicon GPU (no ANE sadly)
#
# quick test portraits (each took 50 steps x 2s / step ~= 100s on my M1 Pro):
# * https://i.imgur.com/5ywISvm.png

rsms

// Find the latest version of this script here:
// https://gist.github.com/rsms/a8ad736ba3d448100577de2b88e826de
// 
const EM = 2048

interface FontInfo {
  familyName :string
  styleName  :string
  unitsPerEm :int
  ascender   :int

coughski

// Variables used by Scriptable.
// These must be at the very top of the file. Do not edit.
// icon-color: deep-blue; icon-glyph: subway;

let GtfsRealtimeBindings = importModule("gtfs-realtime.js") // add this file to Scriptable from https://github.com/MobilityData/gtfs-realtime-bindings/blob/master/nodejs/gtfs-realtime.js
// also add https://github.com/protobufjs/protobuf.js/blob/master/dist/protobuf.min.js to Scriptable
const API_KEY = "paste_your_api_key_here" // get a free MTA API key at https://api.mta.info/#/landing

const ROOT = "entity"
const ALERT = "alert"

coughski

// Variables used by Scriptable.
// These must be at the very top of the file. Do not edit.
// icon-color: blue; icon-glyph: bicycle;

/*** WIDGET SETUP ***
 * Edit the Scriptable widget, select this CitiBike script, and fill the Parameter field with your station's id
 * 
 * HOW TO FIND A STATION'S ID
 * Search for a CitiBike station by street name at https://gbfs.citibikenyc.com/gbfs/en/station_information.json
 * Find the station_id field associated with the name

simonbs

// Variables used by Scriptable.
// These must be at the very top of the file. Do not edit.
// icon-color: brown; icon-glyph: hand-holding-usd;
// To use this script, you must create a personal access token on GitHub with the `read:org// scope.
// Follow the instructions on the link below to create your personal access token.
//
// https://github.com/settings/tokens
//
// Run the script when you ahve created your personal access token. It'll prompt you to enter the token and store it securely in the keychain on the device.

simonbs

// Variables used by Scriptable.
// These must be at the very top of the file. Do not edit.
// icon-color: blue; icon-glyph: apple-alt;
const TITLE = ""
const DATE = "2021-10-18T17:00:00Z"
const BG_IMG_URL = "https://i.ibb.co/YQ09XTW/background.png"
const TITLE_IMG_URL = "https://i.ibb.co/qYH3Y9w/text.png"
const TITLE_IMG_SIZE = new Size(155, 25)
const LINK = "https://www.youtube.com/watch?v=exM1uajp--A"

Rocketknight1

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
import tensorflow as tf

model_name = 'bert-base-cased'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)

texts = ["I'm a positive example!", "I'm a negative example!"]
labels = [1, 0]