wess

# Enable Zsh options for history
setopt EXTENDED_HISTORY
setopt INC_APPEND_HISTORY
setopt SHARE_HISTORY
setopt HIST_FIND_NO_DUPS

# Clear local history and load a fresh session for the current directory
function load_local_history() {
  if [[ -f .zsh_cmd_history ]]; then
    # Clear in-memory history and reload only local history

fractaledmind

class RoutingTest < ActionDispatch::IntegrationTest
  IGNORED_CONTROLLERS = Set[
    "Rails::MailersController"
  ]

  test "no unrouted actions (public controller methods)" do
    actions_by_controller.each do |controller_path, actions|
      controller_name = "#{controller_path.camelize}Controller"
      next if IGNORED_CONTROLLERS.include?(controller_name)

tenderlove

require "strscan"

class Scanner
  TOK = []
  TOK["{".ord] = :LBRACE; TOK["}".ord] = :RBRACE; TOK[";".ord] = :SEMI

  def initialize data
    @scan = StringScanner.new data
    @prev_pos = @scan.pos
  end

basicfeatures

MiniGPT client v1.0

#!/usr/bin/env ruby
# frozen_string_literal: true
#
# MiniGPT client v1.0
#
# gem install --user-install ruby-openai
#

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

tenderlove

#!/Users/aaron/.rubies/arm64/ruby-trunk/bin/ruby

# This is a demo language server for Ruby, written in Ruby.  It just checks
# the syntax of Ruby programs when you save them.
#
# Configure this in Vim by adding the vim-lsp plugin, then doing this
# in your vimrc:
#
#  au User lsp_setup
#        \ lsp#register_server({

claysauruswrecks

# main
llama-index
langchain

veekaybee

ChatGPT Resources

Context

ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowhere. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?

I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.

Model Architecture

dainiax

#!/bin/bash

BASE_PATH=/var/opt/gitlab/gitlab-rails/shared/registry/docker/registry/v2/repositories

DRY_RUN=0
KEEP_LAST_IMAGES=10
RUN_GARBAGE_COLLECTOR=0
GITLAB_CTL_COMMAND=`which gitlab-ctl`

Aetherus

How to scale out a Phoenix application with websocket

Foreword

It's relatively easy to scale out stateless web applications. You often only need a reverse proxy. But for those stateful web applications, especially those applications that embeds websocket services in them, it's always a pain to distribute them in a cluster. The traditional way is introducing some external services like Redis to handle pubsub, however, in such way, you often need to change your code. Can Erlang/Elixir, the "concurrency oriented programming languages", best other languages in this use case? Has Phoenix framework already integrated the solution of horizontally scaling websocket? I'll do an experiment to prove (or disprove) that.

Resources