Maharshi-Pandya

You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

ifeulner

Longhorn best practices

The following settings are provided as an example how longhorn should be configured in a production cluster, especially if it is deployed on Hetzner Cloud infrastructure.

Hetzner server nodes provide local storage and allow up to five attached volumes (with a size of up to 10TiB each) Local storage is provided by NVMe storage and therefore is much faster than the attached volumes, but limited in size (max 300GiB usable).

It is assumed that the cluster creation is already done, e.g. via terraform scripts provided by the great kube-hetzner project.

Initial configuration

mschnetzer

librarian::shelf(tidyverse, janitor, ggstream, ggtext, colorspace, MetBrewer, msthemes)

rawdata <- read.csv2("https://data.statistik.gv.at/data/OGD_vpi15_VPI_2015_1.csv")
coicop <- read.csv2("https://data.statistik.gv.at/data/OGD_vpi15_VPI_2015_1_C-VPI5-0.csv")

inflation <- rawdata %>%
  mutate(datum = ym(str_extract(C.VPIZR.0, "(\\d)+"))) %>%
  left_join(coicop %>% select(code, name), by = c("C.VPI5.0"="code")) %>%
  select(datum, Coicop = name, Beitrag = F.VPIEFVJM) %>%
  drop_na() %>%