JoeyBurzynski

58 bytes of CSS to look great nearly everywhere

When making this website, i wanted a simple, reasonable way to make it look good on most displays. Not counting any minimization techniques, the following 58 bytes worked well for me:

main {
  max-width: 38rem;
  padding: 2rem;
  margin: auto;
}

aescarcha

// Simple gist to test parallel promise resolution when using async / await

function promiseWait(time) {
  return new Promise((resolve, reject) => {
    setTimeout(() => {
        resolve(true);
    }, time);
});
}

1st1

import asyncio
import random
import time


async def worker(name, queue):
    while True:
        # Get a "work item" out of the queue.
        sleep_for = await queue.get()

jasonbunk

#!/bin/bash
set -ex
THISSCRIPTPATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
cd $THISSCRIPTPATH

echo "This script is for building Caffe2 locally (not in /usr/lib) on Ubuntu 16.04"
echo "Can be used for Python virtual environments"
echo "This assumes OpenCV is already installed: https://www.pyimagesearch.com/2016/10/24/ubuntu-16-04-how-to-install-opencv/"

my_python_version=$(python -c "import sys; print('.'.join(map(str, sys.version_info[:2])))")

mjdietzx

#
# written for Amazon Linux AMI
# creates an AWS Lambda deployment package for pytorch deep learning models (Python 3.6.1)
# assumes lambda function defined in ~/main.py
# deployment package created at ~/waya-ai-lambda.zip
#

#
# install python 3.6.1
#

akeaswaran

ESPN's hidden API endpoints

Football

College Football

Latest News: http://site.api.espn.com/apis/site/v2/sports/football/college-football/news

Latest Scores: http://site.api.espn.com/apis/site/v2/sports/football/college-football/scoreboard

fformenti

amorgun

from sqlalchemy.dialects import postgresql

def bulk_upsert(session: Session, 
                items: Sequence[Mapping[str, Any]]):
  session.execute(
    postgresql.insert(MyModel.__table__)
    .values(items)
    .on_conflict_do_update(
      index_elements=[MyModel.id],
      set_={MyModel.my_field.name: 'new_value'},

amitkot

keywords = {
    ["date"] = function() return os.date("%B %d, %Y") end,
    ["name"] = "my name is MISTER",
}

expander = hs.hotkey.bind({"alt"}, "d", nil, function() -- don't start watching until the keyUp -- don't want to capture an "extra" key at the begining
    local what = ""
    local keyMap = require"hs.keycodes".map -- shorthand... in a formal implementation, I'd do the same for all `hs.XXX` references, but that's me
    local keyWatcher
    keyWatcher = hs.eventtap.new({ hs.eventtap.event.types.keyUp, hs.eventtap.event.types.keyDown }, function(ev)

chrisdpa-tvx

All Your Data Does Not Belong In a Database

Businesses are machines producing mountains of data about sales, usage, customer, costs, etc... Traditionally data processing is highly centralised with teams of staff and computer running hot a whirling ready to process. We can do better than moving the mountain of data into the corporate data machine - so long as that machinary is light enough to be moved to the data.

Don't move the mountain - Bring the processing to the data

We've had this problem; a huge directory of files in CSV format, conataining vital information for our business. But it's in CSV, requires analysis, and don't you don't feel like learning sed/grep/awk today - besides it's 2017 and no-one thinks those tools are easy to use.