This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.
To capture the video (filesize: 19MB), using the free "QuickTime Player" application:
| =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') |
| #!/usr/bin/ruby | |
| require 'rss' | |
| # Usage | |
| # $ ./railscasts.rb http://railscasts.com/subscriptions/YOURRAILSCASTRSS/\/ | |
| # episodes.rss | |
| # OR | |
| # $ ./railscasts.rb | |
| p 'Downloading rss index' |
| 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) |
| /* Exercise: Loops and Functions #43 */ | |
| package main | |
| import ( | |
| "fmt" | |
| "math" | |
| ) | |
| func Sqrt(x float64) float64 { | |
| z := float64(2.) |
| 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') } |
| 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 |
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.
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 theA 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.
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.
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.
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.