franz101 franz101

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.

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

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;
}

	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)

	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'},

	#
	# 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
	#

	#!/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])))")

	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()

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

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