OrionReed

// 3D Dom viewer, copy-paste this into your console to visualise the DOM as a stack of solid blocks.
// You can also minify and save it as a bookmarklet (https://www.freecodecamp.org/news/what-are-bookmarklets/)
(() => {
  const SHOW_SIDES = false; // color sides of DOM nodes?
  const COLOR_SURFACE = true; // color tops of DOM nodes?
  const COLOR_RANDOM = false; // randomise color?
  const COLOR_HUE = 190; // hue in HSL (https://hslpicker.com)
  const MAX_ROTATION = 180; // set to 360 to rotate all the way round
  const THICKNESS = 20; // thickness of layers
  const DISTANCE = 10000; // ¯\\_(ツ)_/¯

zats

struct ContentView: View {
    var body: some View {
        let names = [
            ["appstore.app.dashed", "buildings.3d", "emoji.chicken.face"],
            ["person.text.rectangle.and.nfc", "secure.element", "laugh.bubble.tapback.2.he"],
            ["apple.news", "apple.podcasts.square.stack", "apple.slice"],
        ]
        VStack(spacing: 20) {
            Grid(horizontalSpacing: 20, verticalSpacing: 20) {
                ForEach(names, id: \.self) { nameRow in

yoavg

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

rain-1

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

stephancasas

{
  "65536": [
    "alphashift"
  ],
  "131072": [
    "shift"
  ],
  "196608": [
    "alphashift",
    "shift"

JadenGeller

import AppKit

struct EventMonitor: AsyncSequence {
    typealias Element = NSEvent
    
    enum Scope {
        case local
        case global
    }
    var scope: Scope = .local

SMUsamaShah

These are VMs running built with JavaScript/WASM allowing you to run an operating system within your browser, all client side.

Emulators

VMs

1. https://copy.sh/v86/
2. https://webvm.io/
3. https://bellard.org/jslinux/
4. https://jamesfriend.com.au/pce-js/ (https://github.com/jsdf/pce)

kylehowells

//
//  FreeSpaceViewController.swift
//  Free Space
//
//  Created by Kyle Howells on 04/05/2022.
//

import UIKit

class FreeSpaceViewController: UIViewController {

othyn

//
//  App.swift
//
//  Created by Ben Tindall on 30/03/2022.
//

import Foundation
import SwiftUI
import Cocoa

MatthewWaller

import ARKit
import Combine
import RealityKit
import SwiftUI

@main
struct MyApp: App {
    var body: some Scene {
        WindowGroup {
            ContentView()