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@zhengjia
zhengjia / capybara cheat sheet
Created June 7, 2010 01:35
capybara cheat sheet
=Navigating=
visit('/projects')
visit(post_comments_path(post))
=Clicking links and buttons=
click_link('id-of-link')
click_link('Link Text')
click_button('Save')
click('Link Text') # Click either a link or a button
click('Button Value')
@samqiu
samqiu / railscasts.rb
Last active October 18, 2025 19:39
Download free Railscast video
#!/usr/bin/ruby
require 'rss'
# Usage
# $ ./railscasts.rb http://railscasts.com/subscriptions/YOURRAILSCASTRSS/\/
# episodes.rss
# OR
# $ ./railscasts.rb
p 'Downloading rss index'
@jodosha
jodosha / adapter_test.rb
Last active August 11, 2019 03:12
MiniTest shared examples
require 'test_helper'
shared_examples_for 'An Adapter' do
describe '#read' do
before do
@adapter.write(@key = 'whiskey', @value = "Jameson's")
end
it 'reads a given key' do
@adapter.read(@key).must_equal(@value)
@zyxar
zyxar / exercise.tour.go
Last active April 17, 2026 15:45
tour.golang exercise solutions
/* Exercise: Loops and Functions #43 */
package main
import (
"fmt"
"math"
)
func Sqrt(x float64) float64 {
z := float64(2.)
@dergachev
dergachev / GIF-Screencast-OSX.md
Last active July 25, 2026 13:41
OS X Screencast to animated GIF

OS X Screencast to animated GIF

This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.

Screencapture GIF

Instructions

To capture the video (filesize: 19MB), using the free "QuickTime Player" application:

@sgringwe
sgringwe / rails4_paperclip_default_url
Last active March 19, 2019 00:54
rails 4 paperclip asset_path precompiled default_url
I was having trouble setting the default_url for paperclip attachables while using rails 4 asset pipeline fingerprints.
What was not working for some reason:
default_url: ActionController::Base.helpers.asset_path('event_default.jpg')
What worked for me:
default_url: lambda { |image| ActionController::Base.helpers.asset_path('event_default.jpg') }
@stuart11n
stuart11n / gist:9628955
Created March 18, 2014 20:34
rename git branch locally and remotely
git branch -m old_branch new_branch # Rename branch locally
git push origin :old_branch # Delete the old branch
git push --set-upstream origin new_branch # Push the new branch, set local branch to track the new remote
@rgrove
rgrove / README.md
Created February 8, 2016 19:01
Cake's approach to React Router server rendering w/code splitting and Redux

Can't share the complete code because the app's closed source and still in stealth mode, but here's how I'm using React Router and Redux in a large app with server rendering and code splitting on routes.

Server

  1. Wildcard Express route configures a Redux store for each request and makes an addReducers() callback available to the getComponents() method of each React Router route. Each route is responsible for adding any Redux reducers it needs when it's loaded. (This isn't really necessary on the

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
rohitg00 / llm-wiki.md
Last active July 29, 2026 15:14 — forked from karpathy/llm-wiki.md
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory

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.