valk

Rails Models

Generating models

$ rails g model User

Associations

belongs_to

has_one

valk

#!/usr/bin/env bash
# Tested for Ubuntu 12.04

[[ "$(id -u)" -ne "0" ]] && echo "must be root!" && exit 1

RBVER="1.9.3-p194"

apt-get update
apt-get install -y build-essential zlib1g-dev libyaml-dev libssl-dev wget rsync

valk

diff --git a/gc.c b/gc.c
--- a/gc.c
+++ b/gc.c
@@ -77,6 +77,41 @@ void *alloca ();
 #ifndef GC_MALLOC_LIMIT
 #define GC_MALLOC_LIMIT 8000000
 #endif
+#define HEAP_MIN_SLOTS 10000
+#define FREE_MIN  4096
+

valk

=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')

valk

du -k -d1 * | sort -nr | cut -f2 | xargs -d '\n' du -sh

valk

How to access multiple github accounts with different SSH Keys settings

Create different public keys

create different ssh keys according the article Mac Set-Up Git

$ ssh-keygen -t rsa -C "user1@example.com"

valk

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.