IanColdwater

Mute these words in your settings here: https://twitter.com/settings/muted_keywords

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dlaptev

1. Create a new Telegram bot:
   1. start a chat with BotFather;
   2. type /newbot, select a name (to be shown in chats) and handle for your bot;
   3. note the bot token to access HTTP API - a long string with a colon in the middle (later referred to as <token>);
   4. optionally /setdescription and /setuserpic.
2. Add the bot to the chat/channel and note its id:
   1. add the bot to a new or existing chat or group - this is where your bot will send notifications to;
   2. go to https://api.telegram.org/bot<token>/getUpdates (replace <token> with your token);
   3. within the chat part, find and note the id field - a positive or negative number (later referred to as <chat_id>).
3. Programmatically send notifications:

brianshumate

If you'd like to experiment with Terraform on macOS locally, a great provider for doing so is the Docker provider. You can get set up in a few simple steps, like so:

1. Install Docker

Install Docker for Mac if you have not already.

coolaj86

Getting Started with NPM (as a developer)

As easy as 1, 2, 3!

Updated:

• Aug, 08, 2022 update config docs for npm 8+
• Jul 27, 2021 add private scopes
• Jul 22, 2021 add dist tags
• Jun 20, 2021 update for --access=public
• Sep 07, 2020 update docs for npm version

tmckayus

Overview: running crc on a remote server

This document shows how to deploy an OpenShift instance on a server using CodeReady Containers (crc) that can be accessed remotely from one or more client machines (sometimes called a "headless" instance). This provides a low-cost test and development platform that can be shared by developers. Deploying this way also allows a user to create an instance that uses more cpu and memory resources than may be available on his or her laptop.

While there are benefits to this type of deployment, please note that the primary use case for crc is to deploy a local OpenShift instance on a workstation or laptop and access it directly from the same machine. The headless setup is configured completely outside of crc itself, and supporting a headless setup is beyond the mission of the crc development team. Please do not ask for changes to crc to support this type of deployment, it will only cost the team time as they politely decline :)

The instructions here were tested with F

isaacs

echo 'export PATH=$HOME/local/bin:$PATH' >> ~/.bashrc
. ~/.bashrc
mkdir ~/local
mkdir ~/node-latest-install
cd ~/node-latest-install
curl http://nodejs.org/dist/node-latest.tar.gz | tar xz --strip-components=1
./configure --prefix=~/local
make install # ok, fine, this step probably takes more than 30 seconds...
curl https://www.npmjs.org/install.sh | sh

johnie

Filter Phish

This Google Apps Scripts filter will automatically move all future emails with the header X-PHISHTEST to trash.

Getting started

1. Go to Google Apps Script
2. Add a new script and copy paste the content from the sibling file
3. Test run the script, go to View in the menu bar and click Logs

• If there's no errors it should be blank, OR a verbose message that it removed a phishing email.

jakeonrails

data:text/html, <style type="text/css">#e{position:absolute;top:0;right:0;bottom:0;left:0;}</style><div id="e"></div><script src="http://d1n0x3qji82z53.cloudfront.net/src-min-noconflict/ace.js" type="text/javascript" charset="utf-8"></script><script>var e=ace.edit("e");e.setTheme("ace/theme/monokai");e.getSession().setMode("ace/mode/ruby");</script>

cloudnull

TMATE_FILE=$($(command -v python3 || command -v python) <<EOC
import requests
r = requests.get(
    'https://api.github.com/repos/tmate-io/tmate/releases/latest'
)
releases = r.json()
amd64_releases = [
    i for i in releases['assets']
    if 'amd64' in i['name'] and not 'dbg' in i['name']
][0]

jordansissel

logstash queries graphed with graphite.

Operation: Decouple whisper from graphite.

Method: Create a graphite function that does a date histogram facet query against elasticsearch for a given query string for the time period viewed in the current graph.

Reason: graphite has some awesome math functions. Wouldn't it be cool if we could use those on logstash results?

The screenshot below is using logstash to watch the twitter stream of keywords "iphone" "apple" and "samsung" - then I graph them each, so we get an idea of popularity. As a bonus, I also do a movingAverage() on the iphone curve to show you why this is awesome.