sh0oki

#!/usr/bin/python

import hashlib, sys

# Simplified version of https://github.com/drupal/drupal/blob/7.x/includes/password.inc

ALGO = hashlib.sha512

ITOA64 = './0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' # ICHS

cvan

var queryParams = window.location.search.substr(1).split('&').reduce(function (qs, query) {
  var chunks = query.split('=');
  var key = chunks[0];
  var value = decodeURIComponent(chunks[1] || '');
  var valueLower = value.trim().toLowerCase();
  if (valueLower === 'true' || value === 'false') {
    value = Boolean(value);
  } else if (!isNaN(Number(value))) {
    value = Number(value);
  }

zcaceres

'Include' in Sequelize: The One Confusing Query That You Should Memorize

When querying your database in Sequelize, you'll often want data associated with a particular model which isn't in the model's table directly. This data is usually typically associated through join tables (e.g. a 'hasMany' or 'belongsToMany' association), or a foreign key (e.g. a 'hasOne' or 'belongsTo' association).

When you query, you'll receive just the rows you've looked for. With eager loading, you'll also get any associated data. For some reason, I can never remember the proper way to do eager loading when writing my Sequelize queries. I've seen others struggle with the same thing.

Eager loading is confusing because the 'include' that is uses has unfamiliar fields is set in an array rather than just an object.

So let's go through the one query that's worth memorizing to handle your eager loading.

The Basic Query

ywwwtseng

Host react application on Apache server

Step 1 : Create your app

$ npm install -g create-react-app 
$ create-react-app my-app

Step 2 : Build it for production

reZach

// Example card.

// User-editable properties for this card:
export const PROPS = [
    propNumber("ticks", 60),    
    propBoolean("infinite", true),
    propBoolean("completeAnimationDelay", false),
    propNumber("completeAnimationDelayMin", 60),
    propNumber("completeAnimationDelayMax", 60),
    propNumber("forceFrame", -1)

karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.