yunho-c

magick input.png -resize "2880x1800>" -background transparent -gravity center -extent 2880x1800 output.png

yunho-c

# Examples:
# python cubemap_stitcher.py /path/to/your/images
# or
# python cubemap_stitcher.py ./my_cubemap_folder --output ./final_skybox.jpg --width 4096 --ext .jpg


from pathlib import Path
from typing import Optional
import typer
import numpy as np

yunho-c

import bpy
import math

def create_3d_rotation_arrow():
    # Parameters to tweak the look of your arrow
    radius = 2.0             # Radius of the overall circle
    thickness = 0.2          # Thickness of the curved pipe
    arrow_radius = 0.5       # Width of the arrowhead
    arrow_length = 1.0       # Length of the arrowhead
    start_angle = 0.0

yunho-c

import bpy
import bmesh
import math


# ============================================================
# Toon / technical-diagram outline setup using Freestyle
# ============================================================
#
# What this does:

yunho-c

import bpy

def create_toon_shader():
    mat_name = "Toon_3_Tone"
    
    # Create a new material and enable use of nodes
    mat = bpy.data.materials.new(name=mat_name)
    mat.use_nodes = True
    
    # Get the node tree

yunho-c

name: Publish Debian Package to R2

on:
  push:
    tags:
      - 'v*.*.*' # Triggers only when you push a version tag (e.g., v1.0.0)

jobs:
  build-and-publish:
    runs-on: ubuntu-latest

yunho-c

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.

yunho-c

import torch
from transformers import Sam3Processor, Sam3Model
from PIL import Image
import requests

# Target your Nvidia GPU
device = "cuda" if torch.cuda.is_available() else "cpu"

# Automatically download and load SAM 3 to the GPU
print("Loading model...")

yunho-c

"""Simple CLI example: convert a PDF file to Markdown using pdf_oxide."""

from pathlib import Path

import typer

from pdf_oxide import PdfDocument


app = typer.Typer(

yunho-c

<div id="container"></div>
<div id="texture_holder">
<img src='https://s3-us-west-2.amazonaws.com/s.cdpn.io/61062/player_texture.jpg' id="texture_man" crossOrigin="anonymous">
<img src='https://s3-us-west-2.amazonaws.com/s.cdpn.io/61062/trumpet_texture.jpg' id="texture_saxo" crossOrigin="anonymous">
<img src='https://s3-us-west-2.amazonaws.com/s.cdpn.io/61062/stage_texture_base.png' id="texture_stage" crossOrigin="anonymous">
<img src='https://s3-us-west-2.amazonaws.com/s.cdpn.io/61062/piano_texture.jpg' id="texture_piano" crossOrigin="anonymous">
<img src='https://s3-us-west-2.amazonaws.com/s.cdpn.io/61062/drum_texture.jpg' id="texture_drum" crossOrigin="anonymous">
<img src='https://s3-us-west-2.amazonaws.com/s.cdpn.io/61062/trumpet_texture.jpg' id="texture_trumpet" crossOrigin="anonymous">
<img src='https://s3-us-west-2.amazonaws.com/s.cdpn.io/61062/kontrabass_texture.jpg' id="texture_kontrabass" crossOrigin="anonymous">
</div>