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FUNCTION_NAME = 'parallel_lambda'
LAMBDA_ROLE = 'arn:aws:iam::972882471061:role/lambda_exec_role'
DEFAULT_MEMORY = 128
DEFAULT_TIMEOUT = 30
AWS_PROFILE = 'paralambda'
NUM_THREADS = 1000

import boto3
import subprocess
import json

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// Szudzik's Elegant Pairing Function
// http://szudzik.com/ElegantPairing.pdf

function pair(x,y){
  return y > x ? (y*y+x) : (x*x+x+y);
}

function unpair(z){
  var q = Math.floor(Math.sqrt(z)),
	    l = z - Math.pow(q, 2);

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<title>FakeTalk</title>
<style>
body {
    background: #eee;
}
* {
    box-sizing: border-box;
}
.paper {
    padding: 10px;

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// author: Kevin Kwok, based on Rose Curve by Eduard Bespalov
// license: The Software shall be used for Good, not Evil.

function main(params) {
    var radius = 20,
        vec = new CSG.Vector3D(0, 6, 0),
        angle;

    angle = 360 / 4;
    var pent = CSG.Polygon.createFromPoints([

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thing
= (list 1 2 3 4) 
= (1 . (2 . (3 . (4 . nil))))

(car thing)
= 1

(cdr thing)
= (2 . (3 . (4 . nil)))
= cdr_thing

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Experiments with Reverse Mode Auto-Differentiation

Auto Differentiation is a technique used to calculate gradients of arbitrary computer programs. As opposed to symbolic differentiation, which occasionally results in an exponential blow-up in the size of the programs, and numerical differentiation, which estimates the gradient by running the target program dozens or hundreds of times, auto differentiation allows you to get out the gradient of a program after a single pass.

Reverse Mode Auto-Differentiation, especially in its imperative form has recently gained popularity due to projects like TF Eager, PyTorch, and HIPS Autograd. Existing auto differentiation libraries exploit operator overloading capabilities found in many languages to create data structures that incrementally track gradients.

Javascript lacks operator overloads, so defining special data structures loses much of its natural appeal. Rather than thinking about data structures, we can think about functions and how they compose, and how th

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Neural Network XOR

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// awaitable queue
class AwaitableQueue {
    constructor(){
        this.queue = []
        this.resolvers = []
    }
    pop(){
        if(this.queue.length > 0) return Promise.resolve(this.queue.shift());
        return new Promise((resolve, reject) => this.resolvers.push(resolve) )
    }

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// set up a mock HTTP client/server API
// should be pretty easy to replace this with
// the real ones

var endpoints = {}
async function fetch(path, opts){
    let data = await endpoints[path](JSON.parse(opts.body))
    return { async json(){ return data } }
}
async function serve(path, fn){

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import java.util.Scanner;

public class Add implements Comparable<Add>{
    private String name;
    private String lastName;
    private int number;


    public Add(String name, String lastName, int number) {
        this.name = name;