// data lab · computed 2026-10-13 from 4 builders

Every chart, computed from data

Nothing here is hand-entered. Every number comes from graph.json, which is rebuilt from the published builders on every build. Small sample: read patterns as anecdotes until the series grows.

G1

3D globe

how this is computed

Pins at each builder's city (rounded to 2 decimals), diamonds at origin. Arcs: origin to home (dashed), person to person where similarity ≥ 0.35 (thickness = score), and Fred to each (toggle on the Atlas).

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G2

Similarity heatmap

how this is computed

Pairwise weighted Jaccard over themes (w 2), tools (w 1.5), languages, models, hardware class and previous OS (w 1). Rows ordered by average-linkage hierarchical clustering.

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G3

Connection graph

how this is computed

Edges are pairs with similarity ≥ 0.35 plus hand-curated connections (orange). Colors are communities from one-level Louvain modularity: Lee + Michael (cloudflare pages); James + Yasir (old hardware).

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G4

Theme × person matrix

how this is computed

Each builder's themes against the synthesized THEMES list, sorted by prevalence. 12 themes were arrived at independently by 2+ people.

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G5

Trait correlations

linux toy until omarchy↔codexφ 1both: 3/4
looks then ai↔codexφ 1both: 3/4
ai vampire↔codexφ 1both: 3/4
memory is the product↔githubφ 1both: 3/4
memory is the product↔claudeφ 1both: 3/4
memory is the product↔repoφ 1both: 3/4
cost routing↔codexφ 1both: 3/4
local vs frontier↔githubφ 1both: 3/4
local vs frontier↔claudeφ 1both: 3/4
local vs frontier↔repoφ 1both: 3/4
github↔claudeφ 1both: 3/4
github↔repoφ 1both: 3/4
how this is computed

Phi coefficient between binary traits across builders. Listed only when n ≥ 4. n=4: anecdotal, read as patterns to watch, not findings.

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G6

Stack leaderboards

tools
models
how this is computed

Counts per tool, model, language, hardware class, previous OS and how they found Omarchy. Full boards on /stack/.

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G7

Previous OS → Omarchy

how this is computed

Flows from each previous OS (split evenly per person) to the class of their first Omarchy machine, then to their daily driver.

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G8

How long the road was

how this is computed

First computer year, Linux since, Omarchy since: one line per builder.

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G9

Series growth

how this is computed

Cumulative published interviews by date, with 18 booked calls dotted ahead (count only, as of 2026-10-13).

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G10

Countries and timezones

how this is computed

Home countries: US 2, CA 1, SG 1. Origin countries: GB 2, PK 1, US 1. The series spans 14 hours of UTC offsets.

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G11

Quote wall

Agents run as a team

“I never trust, um, one—one AI. I always get one to check the other.”
Lee Shand · L427

“turn it into issues, and then go do a PR stack, solving each issue in sequence.”
Michael Hurley · L198

AI removed the implementation wall

“I'm s*** at implementation”
Lee Shand · L463

“I just built s***, right?”
James Tervit · L965

“I just went in always with the attitude that this is a tool that I'm going to use to my advantage.”
Michael Hurley · L37

“I love to design much more than I love programming, but I'm good at the programming thing.”
Michael Hurley · L154

“there's probably, like, almost 200 repos on there now since January.”
Michael Hurley · L169

“I think I stopped learning in 2004”
Yasir Malik · L189

“And I don't like being dependent on someone.”
Yasir Malik · L195

Filling the app gaps that kept people on Mac

“my Mac can literally just stay closed in the corner.”
Michael Hurley · L301

It feels like being a kid again

“This is how it used to be on BBS with community. Everybody was sharing”
James Tervit · L374

“He's revived the whole, um, thing about community”
James Tervit · L1020

“this thing got me hooked on how I used to be when I was a kid”
Yasir Malik · L75

Linux on the desktop was a toy until Omarchy

“the way that I've been, um, significantly more productive has been when I installed Omarchy”
Lee Shand · L360

“if I don't know any other application that's better than the terminal, um, it's my go-to.”
James Tervit · L359

“Linux felt like a toy until Omarchy 3, really. And then it was, okay, I can use this for work.”
Michael Hurley · L27

Memory and context are the real product

“For people like me, if I don't have AI anymore, I don't have a life.”
Lee Shand · L90

“That's where all my cognitive function lives, is within Obsidian.”
Lee Shand · L82

“My goal with Obsidian is I never want to be in that application”
Lee Shand · L78

The AI Vampire

“This is— this is just fun”
James Tervit · L350

“I just don't feel there are enough hours in a day. That's my only complaint.”
Yasir Malik · L259

“I'm so glad that I'm living in this day and age where technology is just like moving at the pace it is.”
Yasir Malik · L123

The looks get them in, the AI keeps them

“To be honest, the looks were the first thing that got me.”
Yasir Malik · L68

“With Omarchy, any issues that I had, it's AI is built in.”
Yasir Malik · L84

X is hard, sincerity is the answer

“I better get my finger out because I saw your website.”
James Tervit · L184

Omarchy as an accessibility tool

“how you almost don't have to think to use it”
Lee Shand · L154

how this is computed

Each builder's quotes, grouped by the theme they illustrate. Every quote links to its transcript line.

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G12

Trait radar

how this is computed

Share of indicators each builder has on six axes (agents, local AI, design, data, hardware, accessibility), against the community mean. Indicators live in graph.config.yml.

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G13

Most connected, rarest

Michael Hurley3
James Tervit2
Yasir Malik2
Lee Shand1
how this is computed

Most connected: Michael Hurley (degree 3, strength 1.207). Items used by exactly one builder: blip (Michael), cesium (James), claude-code (Lee), cloudflare-tunnel (James), herdr (Yasir), hermes (Lee), nuphy-node-100 (Yasir), obsidian (Lee), omadesign (Michael), proxmox (James), readwise (Lee), t3code (Michael), vercel (Michael), gpt (Yasir), grok (Michael), bash (James), html-css (Michael), python (Yasir), rust (Michael), sql (Yasir), typescript (Michael), vba (Yasir).

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G14

Talk time and distinctive words

how this is computed

Share of words spoken by the builder in their cleaned transcript (they should dominate), and top TF-IDF terms per builder over the cleaned transcripts (stopwords removed, terms said 3+ times).

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