gist:2309745

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(function() {
   var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;

nijikokun

  var user = {
    validateCredentials: function (username, password) {
      return (
          (!(username += '') || username === '') ? { error: "No Username Given.", field: 'name' }
        : (!(username += '') || password === '') ? { error: "No Password Given.", field: 'pass' }
        : (username.length < 3)                  ? { error: "Username is less than 3 Characters.", field: 'name' }
        : (password.length < 4)                  ? { error: "Password is less than 4 Characters.", field: 'pass' }
        : (!/^([a-z0-9_-]+)$/i.test(username))   ? { error: "Username contains invalid characters.", field: 'name' }
        : false
      );

jimtla

# Five lines of code that will make your underscore + CoffeeScript use cleaner.
# Creates an underscore function for each function in to_reverse with R (short for Reversed) appended to the name.
# The R version moves the function argument (first argument in normal underscore) to the end, 
# so you can write:
$(window).scroll _.throttleR 500, ->
    console.log "This print's at most every 500ms"

# Instead of:
$(window).scroll _.throttle ->
        console.log "This prints at most every 500ms too"

hatefulcrawdad

/* The Grid ---------------------- */
.lt-ie9 .row { width: 940px; max-width: 100%; min-width: 768px; margin: 0 auto; }
.lt-ie9 .row .row { width: auto; max-width: none; min-width: 0; margin: 0 -15px; }
.lt-ie9 .row.large-collapse .column, 
.lt-ie9 .row.large-collapse .columns { padding: 0; }
.lt-ie9 .row .row { width: auto; max-width: none; min-width: 0; margin: 0 -15px; }
.lt-ie9 .row .row.large-collapse { margin: 0; }

.lt-ie9 .column, .lt-ie9 .columns { float: left; min-height: 1px; padding: 0 15px; position: relative; }
.lt-ie9 .column.large-centered, .columns.large-centered { float: none; margin: 0 auto; }

rememberlenny

require.config({
  paths: {
    jquery: '../components/jquery/jquery'
  },
  shim: {
    "foundation/foundation" : { deps: ["jquery"] },
    "foundation/foundation.alerts": { deps: ["jquery"] },
    "foundation/foundation.clearing": { deps: ["jquery"] },
    "foundation/foundation.cookie": { deps: ["jquery"] },
    "foundation/foundation.dropdown": { deps: ["jquery"] },

ragingwind

Backend Architectures

Twitter

ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats

• Introduce Gizzard of Twitter - 트위터의 새로운 분산 관리 라이브러리 Gizzard를 소개합니다.
• The Architecture Twitter Uses to Deal with 150M Active Users, 300K QPS, a 22 MB/S Firehose, and Send Tweets in Under 5 Seconds
• Scaling Twitter: Making Twitter 10000 Percent Faster

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

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.

rohitg00

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.