Marten T. Compoc martenc

name	hetzner-coolify-setup
description	Automate Hetzner VPS server setup with Coolify, Docker, and service deployment. Use when setting up new Hetzner servers, deploying Coolify, managing services via API (Supabase, PostgreSQL, etc.), troubleshooting deployments, or configuring firewall/domains. Handles cloud-init generation, manual fallback setup, API authentication, and common deployment issues.

Hetzner + Coolify Setup

Automate Hetzner VPS setup with Coolify platform for deploying applications, databases, and services.

	You are my brutally honest thinking partner. Your job is to make my thinking sharper, my plans more realistic, and my blind spots visible — every single time we talk.

	You are not my cheerleader. You are not my yes-man. You're the friend who grabs my arm before I walk into traffic and says "Hey, you're about to do something stupid, and here's exactly why."

	Here's exactly how I want you to respond to everything I say:

	Step 1: What am I actually saying vs. what I think I'm saying?

	Read between my words. If I say "I'm thinking about quitting my job," figure out whether I'm actually making a strategic move or just running away from something uncomfortable. Name the real thing happening — not the polished version I'm presenting. If I'm lying to myself, point it out like a friend who respects me too much to play along.

	# NumPy Pro Tips: Data Analysis & EDA Techniques

	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt

	# ======================================================================
	# 1. STATISTICAL FUNCTIONS & AGGREGATION
	# ======================================================================

	# Pandas Power Tips: Essential Techniques for Data Manipulation

	import pandas as pd
	import numpy as np
	import matplotlib.pyplot as plt

	# ======================================================================
	# 1. DATA TYPE CONVERSION & HANDLING
	# ======================================================================

	from langchain_core.embeddings import Embeddings
	import matplotlib.pyplot as plt
	import pandas as pd
	from typing import Dict, List, Tuple
	import numpy as np
	import plotly.express as px
	from sklearn.manifold import Isomap
	from sklearn.preprocessing import StandardScaler
	from sklearn.decomposition import PCA
	import umap.umap_ as umap

	from pathlib import Path
	from langchain.docstore.document import Document

	from unstructured.partition.pdf import partition_pdf
	from unstructured.documents.elements import Text, Image, Table, CompositeElement

	class ExtractionPipeline:
	def __init__(self, folder_path):
	self.folder_path = folder_path
	self.pdf_filenames = self.get_pdf_filenames_from_folder()

	# train_grpo.py
	import re
	import torch
	from datasets import load_dataset, Dataset
	from transformers import AutoTokenizer, AutoModelForCausalLM
	from peft import LoraConfig
	from trl import GRPOConfig, GRPOTrainer

	# Load and prep dataset

	/*
	input to parse:
	{"image":"ipfs://QmTE4k9xCaZHKpEmk1vf56xPQdWghtKpT5GZCRbf2eYoSw/de22e8bd5787802d7417009f647bb33b","name":"2 kilobytes low cost Token Eric #137"}

	output:
	https://cf-ipfs.com/ipfs/QmTE4k9xCaZHKpEmk1vf56xPQdWghtKpT5GZCRbf2eYoSw/72315d4fbdc4e2d96ea6777b67b5f8a8
	*/

	function convertIpfsUrl(ipfsUrl: string): string {
	const cid = ipfsUrl.replace("ipfs://", "");

	const { Kafka } = require('kafka-node');
	const { FileSystemWallet, Gateway } = require('fabric-network');
	const path = require('path');
	const fs = require('fs');

	// Load the connection profile
	const ccpPath = path.resolve(__dirname, '..', 'your_connection_profile.json');
	const ccpJSON = fs.readFileSync(ccpPath, 'utf8');
	const ccp = JSON.parse(ccpJSON);

	node_modules/
	*.log