metafeather’s gists

metafeather / The original rant

Created October 2, 2019 07:55 — forked from dexterous/original-rant.md

Steve Yegge's SOA post

	Stevey's Google Platforms Rant

	I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

	I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't really have SREs and they make engi

metafeather / dimensionality-reduction-techniques.py

Created June 2, 2019 21:17

	# Missing Value Ratio: If the dataset has too many missing values, we use this approach to reduce the number of variables. We can drop the variables having a large number of missing values in them
	# Low Variance filter: We apply this approach to identify and drop constant variables from the dataset. The target variable is not unduly affected by variables with low variance, and hence these variables can be safely dropped
	# High Correlation filter: A pair of variables having high correlation increases multicollinearity in the dataset. So, we can use this technique to find highly correlated features and drop them accordingly
	# Random Forest: This is one of the most commonly used techniques which tells us the importance of each feature present in the dataset. We can find the importance of each feature and keep the top most features, resulting in dimensionality reduction
	# Both Backward Feature Elimination and Forward Feature Selection techniques take a lot of computational time and are thus generally used on smal

metafeather / typed.es6

Last active May 5, 2017 12:38

Examples of VSCode 1.12.1 TypeScript Types inferred from ES6 code

	{
	/*
	* Examples of VSCode 1.12.1 TypeScript Types inferred from ES6 code.
	* Conclusion: You are not required to use JSDoc or TypeScript
	* annotations in most cases if you use default values in functions.
	*/

	let easy = 'abc';

	// Error: Type '123' is not assignable to type 'string'

metafeather / main.go

Created April 27, 2017 13:06

Get goroutine id for debugging

	# ref: https://play.golang.org/p/OeEmT_CXyO
	package main

	import (
	"fmt"
	"runtime"
	"strconv"
	"strings"
	"sync"
	)

metafeather / urls.txt

Created February 1, 2017 15:45

Paths to test a web server with

metafeather / Windows product key

Created July 29, 2015 15:06

	// run the following command in CMD to find the product key associated with your Windows installation:
	wmic path softwarelicensingservice get OA3xOriginalProductKey

metafeather / .gitignore

Last active August 29, 2015 14:04 — forked from brianm/.gitignore

RPC over SSH as described at http://skife.org/go/2013/02/08/rpc_with_ssh_and_domain_sockets.html

metafeather / map.go

Last active August 29, 2015 14:04

MapReduce in Go from JSON via stdin

	// MAPPER
	// ref: http://open.blogs.nytimes.com/2014/07/10/emr-streaming-in-go/

	package main

	import (
	"bufio"
	"encoding/json"
	"fmt"
	"log"

metafeather / map.py

Created July 22, 2014 11:46

MapReduce in Python from JSON via stdin

	#!/usr/bin/python

	# MAPPER
	# ref: http://open.blogs.nytimes.com/2014/07/10/emr-streaming-in-go/

	import sys
	import simplejson as json

	def main():

metafeather / execute_notebook.py

Last active August 29, 2015 14:03

IPython - executing other notebooks

	import io
	from IPython.nbformat import current

	def execute_notebook(nbfile):
	with io.open(nbfile) as f:
	nb = current.read(f, 'json')
	ip = get_ipython()
	for cell in nb.worksheets[0].cells:
	if cell.cell_type != 'code':
	continue

Liam Clancy metafeather