Liam Clancy metafeather

Wrapper script to convert markdown-link-check stdout to lsp_json format for use by Trunk.io VSCode Extension

File locations:

<repo>/.markdown-link-check.json
/.trunk/bin/markdown-link-check.js

	<template xmlns:v-slot="http://www.w3.org/1999/XSL/Transform">
	<div id="graphiql" style="height: 100vh"></div>
	</template>

	<script>
	// Load GraphiQL React based UI within a Vue Component from CDN
	// ref: https://github.com/graphql/graphiql/tree/main/packages/graphiql#cdn-bundle
	const js = (id, url) =>
	new Promise((resolve, reject) => {
	if (document.getElementById(id)) {

	# 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

	{
	/*
	* 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'

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

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

	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

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

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

	package main

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