leodutra

#!/usr/bin/env bash

# =========================================================
# OMARCHY + WINDOWS SECURE BOOT GUIDE
# Limine + sbctl + Custom Keys
# =========================================================
#
# WHAT THIS DOES:
# - Enables Secure Boot on Omarchy
# - Keeps Windows bootable

leodutra

sudo pacman -S --needed base-devel git pkgconf cmake make gcc

# Install Paru
if ! command -v paru >/dev/null 2>&1; then
  git clone https://aur.archlinux.org/paru.git
  cd paru || exit
  makepkg -si
fi

# Install Fabric AI + dependencies

leodutra

#!/usr/bin/env bash

# sets MAKEFLAGS in /etc/makepkg.conf
# so makepkg (AUR/package builds) uses all CPU cores minus one for faster compilation
sudo bash -c 'grep -q "^MAKEFLAGS=" /etc/makepkg.conf && sed -i "/^MAKEFLAGS=/c\MAKEFLAGS=\"-j$(( $(nproc) - 1 ))\"" /etc/makepkg.conf || echo "MAKEFLAGS=\"-j$(( $(nproc) - 1 ))\"" >> /etc/makepkg.conf'

# ------------------------------------------------------------
# Helpers
# ------------------------------------------------------------
zshrc_append() {

leodutra

#!/usr/bin/env bash
set -euo pipefail

# ============================================================
# Rust development environment bootstrap (Arch Linux + zsh)
# ============================================================

# ------------------------------------------------------------
# Helpers
# ------------------------------------------------------------

leodutra

Install steps

# update the system
sudo apt update && sudo apt upgrade -y

# install minimal dev stuff
sudo apt install -y \
	build-essential \
	ca-certificates \

leodutra

Enable Long Paths on Windows

Instructions for Windows 11 and Windows 10

Windows 11

Method 1: Registry Editor (Most Reliable)

1. Press Win + R, type regedit, and press Enter
2. Navigate to: HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem
3. Find LongPathsEnabled (create it if missing as DWORD)

leodutra

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.

leodutra

/**
 * Bitwise Mathematics Utilities
 * 
 * A collection of fast bitwise operations for integer mathematics.
 * These functions prioritize performance over readability and should be used
 * when micro-optimizations are critical (game engines, real-time applications).
 * 
 * References:
 * - http://michalbe.blogspot.com.br/2013/03/javascript-less-known-parts-bitwise.html
 * - http://jsperf.com/bitwise-vs-math-object

leodutra

Ubuntu

Mount rclone sync

This config allows us to mount obsidian folder locally, preventing issues with Google Drive hashes

# create obsidian folder on Google Drive, then
# install rclone
curl https://rclone.org/install.sh | sudo bash

leodutra

const getElementsByText = (text, parent) => {
  if (!text) return []
  const xpath = `//*[text()='${text}']`
  const xpathResult = document.evaluate(
    xpath,
    parent || document,
    null,
    XPathResult.ORDERED_NODE_SNAPSHOT_TYPE,
    null
  )