defunkt

# Video: http://rubyhoedown2008.confreaks.com/08-chris-wanstrath-keynote.html

Hi everyone, I'm Chris Wanstrath.

When Jeremy asked me to come talk, I said yes.  Hell yes.  Immediately.  But
then I took a few moments and thought, Wait, why?  Why me?  What am I supposed
to say that's interesting?  Something about Ruby, perhaps.  Maybe the
future of it.  The future of something, at least.  That sounds
keynote-y.

nkbt

{
  "env": {
    "browser": true,
    "node": true,
    "es6": true
  },

  "plugins": ["react"],

  "ecmaFeatures": {

r-brown

# install Homebrew
$ su ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

# show brew commands
$ brew help

# check current user
$ echo $(whoami)

# grant access to the folders

chrismccord

Phoenix 1.4.x to 1.5.0 upgrade instructions

Phoenix 1.5 requires Elixir >= 1.7. Be sure your existing version is up to date by running elixir -v on the command line.

Install the new phx.new project generator

$ mix archive.uninstall phx_new
$ mix archive.install hex phx_new 1.5.0

arvenil

Create data table and partition it by state

CREATE TABLE data (
   id bigserial not null,
   state smallint not null DEFAULT 1,
   updated_at timestamp without time zone default now()
) partition by list(state);
create table data_pending partition of data for values in (1);
create table data_processing partition of data for values in (2);

novaugust

# for an example of a suggested `locals_without_parens`, see z_locals_without_parens.exs
# parsing and expanding a formatter.exs file would be a good route too
opts = [sourceror_opts: [locals_without_parens: [...], line_length: 122]]

for transformation <- [&PipeChainStart.run/1, &SinglePipe.run/1] do
  ProjTraversal.transform("../my_codebase/", transformation, opts)
end

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.