Andre Vallestero AndreVallestero

Install Vulkan from https://vulkan.lunarg.com/sdk/home (only the core is required, leave everything unchecked)
(Re)Install llama-cpp-python (python venv is recommended, but not necessary)

For PowerShell
```
$env:CMAKE_ARGS="-DGGML_VULKAN=1"
pip install llama-cpp-python --no-cache-dir --force-reinstall -v
```
For CMD/Batch

	vim.g.mapleader = " "
	vim.g.maplocalleader = " "

	vim.g.have_nerd_font = true

	-- Show relative line numbers
	vim.opt.number = true
	vim.opt.relativenumber = true

	-- Enable mouse mode, can be useful for resizing splits for example!

	Install homebrew
	Brew install neovim
	Brew install iterm2
	Brew install nerdfont ubuntu mono
	Install NVChad
	Download gruvbox config for iterm2
	Make iterm2 default
	Install enable LSP support for Go and Python
	Install powerline tty
	- source "/opt/homebrew/opt/kube-ps1/share/kube-ps1.sh"

	/* ~/Library/KeyBindings/DefaultKeyBinding.Dict

	This file remaps the key bindings of a single user on Mac OS X 10.5 to more
	closely match default behavior on Windows systems. Recommend also rebinding Globe to Control

	Here is a rough cheatsheet for syntax.
	Key Modifiers
	^ : Ctrl
	$ : Shift
	~ : Option (Alt)

	import numpy as np
	import math
	from PIL import Image, ImageDraw
	import cv2

	PPI = 100
	PPMM = round(PPI / 25.4)
	PLATE_WIDTH = 220 # ender 3 220mm
	BUILD_HEIGHT = 150 #250 # ender 3 250mm

	/*
	Simple benchmark for agent vision.

	This is specifically for finding the closest agent, which is not a very common use case in
	simulations. Most simulations use the data from each ray as an input parameter for their
	agents' neural net. This benchmark was made for buddhaman/AnymaEngine, which only takes
	the closest friendly and aggressive agent as inputs into the neural net. The results for
	32768 agents * 10 iterations without chunking are as follows (smaller is better):

	- 9.039s raycast: this is the old method used by AnymaEngine

	{
	"version": 1,
	"language": "English",
	"hideout_name": "Stately Hideout",
	"hideout_hash": 31968,
	"doodads": {
	"Stash": {
	"hash": 3230065491,
	"x": 268,
	"y": 250,

	<!DOCTYPE html>
	<html lang=en>
	<head>
	<meta http-equiv=content-type content="text/html;charset=UTF-8">

	<!--SEO tags -->
	<title>My Page Title</title>
	<meta name="description" content="Benchmarks for measuring the performance of GUI toolkits">
	<meta name="keywords" content="my, key, words, key words, keywords">
	<meta name="twitter:card" content="summary">

	#!/bin/bash
	#
	# Expects two arguments: a pip package and a target directory. Installs the pip
	# package and removes unnecessary build artifacts from the resulting package.
	#
	# See documentation on ./build-layers.sh for more information.

	set -e
	set -x