ADisAD Labs
Reference

Korean lifestyle types: how a parallel life is assembled

If I Were Korean turns ten answers into one fictional setting. Nothing is guessed about you; every line comes from a fixed table, and this page shows the tables.

The name

Your age band picks a generation of names (a 2020s child is a Seo-jun or a Ha-yun; their parents are a Ji-hoon or a Min-ji; the grandparents a Jung-hoon or a Mi-young), your name lean picks the gender statistics to follow, and your Saturday picks the feel. The family name is drawn by how common it is in Korea. The full dataset is on Korean name meanings.

The hometown and the current city

Two separate matches against the eleven living zones. The hometown comes from the climate you grew up loving and how urban your childhood was. The current city comes from where you want to live now, your pace, your food, your nights and how you get around - and it is nudged away from the hometown, because "born in Daegu, lives in Seoul" is the shape of most real Korean lives.

Work and commute

Six kinds of work, each with four concrete jobs (a marketing team that leaves at six; a ten-person app studio in Seongsu; a hanok carpentry crew; a bakery that sells out by noon). Four commutes, with a Korean texture: a webtoon per subway stop, a coast road nobody calls a commute.

The weekend, the food, the hobby

Your Saturday choice selects a routine and a hobby; the current city adds one of its own weekend places. Your plate selects two dishes and the city adds a local one. The lines are written to be looked up: Jagalchi, Sungsimdang, Anmok beach, Hwangnidan-gil.

The life type

One sentence, picked by pace, city and Saturday: the night-owl megacity type, the sea-facing slow-life type, the weekend hiker, the market eater, the home-base type, the steady-rhythm type. It describes a rhythm of days, never a character.

Make your parallel life →

Fictional and for entertainment only. Not a statement about anyone’s identity, ethnicity, gender or personality. Names and regions are playful combinations from a fixed dataset, not stereotypes.