⚠️ Note 2023-01-21
Some things have changed since I originally wrote this in 2016. I have updated a few minor details, and the advice is still broadly the same, but there are some new Cloudflare features you can (and should) take advantage of. In particular, pay attention to Trevor Stevens' comment here from 22 January 2022, and Matt Stenson's useful caching advice. In addition, Backblaze, with whom Cloudflare are a Bandwidth Alliance partner, have published their own guide detailing how to use Cloudflare's Web Workers to cache content from B2 private buckets. That is worth reading,
class HotInputDataUnknown1 { | |
0x4 | |
0x8 | |
0x10 | |
} | |
class HotInputData { | |
UInt32 unknown; // + 0x0 | |
UInt32 modifiers; // + 0x4 | |
UInt32 keyCode; // + 0x8 |
struct Keycode { | |
// Layout-independent Keys | |
// eg.These key codes are always the same key on all layouts. | |
static let returnKey : UInt16 = 0x24 | |
static let enter : UInt16 = 0x4C | |
static let tab : UInt16 = 0x30 | |
static let space : UInt16 = 0x31 | |
static let delete : UInt16 = 0x33 | |
static let escape : UInt16 = 0x35 |
(function (context, trackingId, options) { | |
const history = context.history; | |
const doc = document; | |
const nav = navigator || {}; | |
const storage = localStorage; | |
const encode = encodeURIComponent; | |
const pushState = history.pushState; | |
const typeException = 'exception'; | |
const generateId = () => Math.random().toString(36); | |
const getId = () => { |
// Inspired by https://twitter.com/coderitual/status/1112297299307384833 and https://tapajyoti-bose.medium.com/7-killer-one-liners-in-javascript-33db6798f5bf | |
// Remove any duplicates from an array of primitives. | |
const unique = [...new Set(arr)] | |
// Sleep in async functions. Use: await sleep(2000). | |
const sleep = (ms) => (new Promise(resolve => setTimeout(resolve, ms))); | |
// or | |
const sleep = util.promisify(setTimeout); |
import AppKit | |
struct EventMonitor: AsyncSequence { | |
typealias Element = NSEvent | |
enum Scope { | |
case local | |
case global | |
} | |
var scope: Scope = .local |
import SwiftUI | |
#if os(macOS) | |
public typealias Font = NSFont | |
public typealias FontDescriptor = NSFontDescriptor | |
#else | |
public typealias Font = UIFont | |
public typealias FontDescriptor = UIFontDescriptor | |
#endif |
These are VMs running built with JavaScript/WASM allowing you to run an operating system within your browser, all client side.
Yoav Goldberg, April 2023.
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
:root { | |
--ease-in-quad: cubic-bezier(.55, .085, .68, .53); | |
--ease-in-cubic: cubic-bezier(.550, .055, .675, .19); | |
--ease-in-quart: cubic-bezier(.895, .03, .685, .22); | |
--ease-in-quint: cubic-bezier(.755, .05, .855, .06); | |
--ease-in-expo: cubic-bezier(.95, .05, .795, .035); | |
--ease-in-circ: cubic-bezier(.6, .04, .98, .335); | |
--ease-out-quad: cubic-bezier(.25, .46, .45, .94); | |
--ease-out-cubic: cubic-bezier(.215, .61, .355, 1); |