jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

joegoggins

" Use Vim settings, rather then Vi settings (much better!).
" This must be first, because it changes other options as a side effect.
set nocompatible

" ================ General Config ====================

set number                      "Line numbers are good
set backspace=indent,eol,start  "Allow backspace in insert mode
set history=1000                "Store lots of :cmdline history
set showcmd                     "Show incomplete cmds down the bottom

samueljon

#!/bin/bash

brew install wine-stable winetricks

WINEARCH=win32 WINEPREFIX=~/.wine winecfg
mkdir ~/.cache/winetricks/

winetricks -q dotnet45 corefonts

skanev

#!/usr/bin/env ruby

# A sneaky wrapper around Rubocop that allows you to run it only against
# the recent changes, as opposed to the whole project. It lets you
# enforce the style guide for new/modified code only, as opposed to
# having to restyle everything or adding cops incrementally. It relies
# on git to figure out which files to check.
#
# Here are some options you can pass in addition to the ones in rubocop:
#

gbuesing

UPDATE a fork of this gist has been used as a starting point for a community-maintained "awesome" list: machine-learning-with-ruby Please look here for the most up-to-date info!

Resources for Machine Learning in Ruby

Gems

• liblinear-ruby: Ruby interface to LIBLINEAR using SWIG

vasanthk

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

danijar

# Working example for my blog post at:
# http://danijar.com/variable-sequence-lengths-in-tensorflow/
import functools
import sets
import tensorflow as tf
from tensorflow.models.rnn import rnn_cell
from tensorflow.models.rnn import rnn


def lazy_property(function):

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.