Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.
Avoid being a link dump. Try to provide only valuable well tuned information.
Neural network links before starting with transformers.
{ name: 'Lighting', options: [ | |
'golden hour, warm glow' | |
'blue hour, twilight, ISO12000' | |
'midday, direct lighting, overhead sunlight' | |
'overcast, whitebox, flat lighting, diffuse' | |
'dreamlike diffuse ethereal lighting' | |
'dramatic lighting, dramatic shadows, illumination' | |
'studio lighting, professional lighting, well-lit' | |
'flash photography' | |
'low-key lighting, dimly lit' |
/* | |
* Very simple test runner for nodejs: | |
* | |
* Supports: | |
* | |
* before, after, beforeAll, afterAll | |
* fixture object passed to each test, that before/after/beforeAll/afterAll can modify | |
* -[t]est option on command line to pick tests to run | |
* -[l]inear option on command to disable parallel | |
* built in fixture logger, captures log lines, adds line numbers/file names/timestamps |
const WebSocket = require('ws'); | |
const http = require('http'); | |
const Y = require('yjs'); | |
const wsUtils = require('./utils'); | |
const cookie = require('cookie'); | |
const QuillDelta = require('quill-delta'); | |
//const QuillConverter = require('node-quill-converter'); | |
//const MdastFromQuillDelta = require('mdast-util-from-quill-delta'); |
const getDSLValue = (iterator, last) => { | |
const {value, done} = iterator.next(last); | |
if (done) { | |
return value.slice(1).reduce((x, f) => f(x), value[0]); | |
} | |
return getDSLValue(iterator, last ? [...last, value] : [value]); | |
} | |
const pipe = gen => getDSLValue(gen(null, )); | |
const getDSLValue = (iterator, last) => { | |
const {value, done} = iterator.next(last); | |
if (done) { | |
return value; | |
} | |
switch (value) { | |
case 'sword': { | |
return getDSLValue(iterator, { | |
weaponType: "Shiny Sword", |
const express = require('express') | |
const graphqlHTTP = require('express-graphql') | |
const makeExecutableSchema = require('graphql-tools').makeExecutableSchema | |
const schema = makeExecutableSchema({ | |
typeDefs: ` | |
type Query { | |
user: User | |
} |
How to make an application icon for macOS using
iconset
&iconutil
const I = x => x | |
const K = x => y => x | |
const A = f => x => f (x) | |
const T = x => f => f (x) | |
const W = f => x => f (x) (x) | |
const C = f => y => x => f (x) (y) | |
const B = f => g => x => f (g (x)) | |
const S = f => g => x => f (x) (g (x)) | |
const S_ = f => g => x => f (g (x)) (x) | |
const S2 = f => g => h => x => f (g (x)) (h (x)) |