aymericbeaumet

Ever wanted to delete all your likes/favorites from Twitter but only found broken/expensive tools? You are in the right place.

1. Go to: https://twitter.com/{username}/likes
2. Open the console and run the following JavaScript code:

setInterval(() => {
  for (const d of document.querySelectorAll('div[data-testid="unlike"]')) {
    d.click()
 }

Zhentar

using System;
using System.Collections;
using System.Collections.Generic;
using System.Linq;
using System.Runtime.CompilerServices;

public abstract class BaseDictionary<TKey, TValue>
{
	protected BaseDictionary() { }

Zhentar

/* This Source Code Form is subject to the terms of the Mozilla Public
 * License, v. 2.0. If a copy of the MPL was not distributed with this
 * file, You can obtain one at http://mozilla.org/MPL/2.0/.
 *
 * The Initial Developer of the Original Code is Rune Skovbo Johansen.
 * Portions created by the Initial Developer are Copyright (C) 2015
 * the Initial Developer. All Rights Reserved.
 */

using System;

NikolayS

-- Installs "file_fdw" extension and creates foreign table to work with data from CSV file.
-- See also the comment below which helps to automate the process for Google Spreadsheets
-- Another option would be using Multicorn for Google Spreadsheets, but it requires additional steps  
-- (see https://wiki.postgresql.org/wiki/Foreign_data_wrappers).

create extension file_fdw;
create server "import" foreign data wrapper file_fdw;

create foreign table "table1" (
  col1 text,

anuraj

using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;
using Microsoft.AspNetCore.Builder;
using Microsoft.AspNetCore.Hosting;
using Microsoft.AspNetCore.Http;
using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Logging;
using WebApp2.Middlewares;

tartakynov

import numpy as np
import pylab as pl
from numpy import fft
    
def fourierExtrapolation(x, n_predict):
    n = x.size
    n_harm = 10                     # number of harmonics in model
    t = np.arange(0, n)
    p = np.polyfit(t, x, 1)         # find linear trend in x
    x_notrend = x - p[0] * t        # detrended x