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12+ years in Ruby. Payment systems, search infrastructure, eCommerce platforms, and now AI-powered applications that just work.

Quintin Adam

Quintin Adam

Software Engineer

Remote / USA
quintin-ai — ruby llm_assistant.rb
zsh

Loading quintin-ai v11.42.1...

Model: claude-sonnet-4.6 · tools: experience, stats, skills, background

Context: ~/quintin/experience/*.md

Type help for commands (ls, cat, stats…) — or just ask me anything.

─────────────────────────────────────────

quintin-ai: Hello! I'm Quintin's AI assistant. Ask me anything about his experience, skills, or request a custom resume.

Suggested queries:

responses generated from quintin's professional context | powered by Claude

Experience

About

I didn't come to code in a straight line. I started as an audio engineer in Michigan, spent a few years producing and DJing electro house (the high point: Deadmau5 playing his own unofficial remix of one of my tracks while I was on stage), then taught skiing at Deer Valley. The real turn came during a slow summer working a sales job at Best Buy — bored out of my mind, I decided to learn to program, and the moment I found Ruby I never looked back.

That new skill became a new career, and the career became freedom: I could build great products from anywhere in the world. So I sold almost everything, put my Tesla Model S in storage, and spent the next five years as a digital nomad, working my way through Central and South America, Asia, and Europe. Eight months on an island in Thailand turned into a meditation and yoga practice I still keep every morning.

The thread through all of it is teaching and mentoring. Production software, a ski slope, a handstand, a junior engineer's first pull request — the part I love is the moment something clicks for someone else. When I landed in Osaka I started an AcroYoga meetup to find my people, and some of them are my closest friends today. Code just turned out to be the medium that stuck.

Now I'm in Osaka with my wife and a new baby, thirty minutes from a hiking trail one way and the center of the city the other. That balance, deep technical focus next to a real life away from the screen, is the whole point for me.

And I genuinely love this work. Ruby and Rails let me take an idea and have it running in production by the end of the day with almost no ceremony, and after twelve years that still feels like a kind of magic. Lately the ceiling has moved again: I've woven AI into nearly every part of how I build — writing, reviewing, debugging, reading unfamiliar code, and designing the agent tooling my team works inside every day — and it's unlocked a level of performance and ambition I didn't have a year ago. We're at the start of the biggest shift in software I've seen in my career, and I intend to be building right at the front of it.

Coding Activity

12.9K
Commits
2.3M
Lines Added
1.0M
Net Lines

Commits per week

All projects combined · each cell is one week

Less
More

Anatomy of a Request

Pick a question and watch a real, instrumented trace of how the AI terminal answers it — guardrail screen → Claude call → tool execution → final response, each span timed on one timeline like an OpenTelemetry waterfall. These are live API calls (cached up to 60s), not a recording.

Trace

▶ pick a question above to run a live trace

Model answer

request guardrail llm tool

Real-User Vitals

This page's own Core Web Vitals, measured live in your browser via the Performance API and graded against Google's thresholds — the same client-side signal a RUM agent collects. Nothing is sent anywhere; it's computed locally, right here.

TTFB
Time to First Byte
FCP
First Contentful Paint
LCP
Largest Contentful Paint
INP
Interaction to Next Paint
CLS
Cumulative Layout Shift
Good Needs work Poor · INP appears once you interact

Live Logs

Structured events from this app, tailing in real time — a miniature of Better Stack's Log Management. Click around or run a trace above and watch the requests land. Only method, path, status, and timing are logged — never your input.

~/logs — tail -f

Service Level Objective

A 99.90% availability target for quintinadam.io over a rolling 30-day window, with the error budget computed from real Better Stack downtime — the way an SRE actually watches a service.

99.90%
Target
100%
30-day actual
0.0×
Burn rate
Error budget remaining 43.2 / 43.2 min

Meeting SLO · 0 min downtime used of 43.2 min budget · 30-day window · source: Better Stack

Incident Post-mortem

Pick a real, resolved incident from one of this site's own dependencies and Claude writes a blameless post-mortem from the public status timeline — mirroring Better Stack's AI post-mortems. Grounded in the actual updates; nothing invented.

loading recent incidents…

Post-mortem

▶ choose an incident above to generate a post-mortem

Query me over MCP

The same read-only tools this site's AI assistant uses are exposed as a Model Context Protocol server. Add it to Claude Code (or any MCP client) and ask about my experience, skills, or this site's live status from your own terminal.

add to Claude Code
claude mcp add --transport http quintin https://www.quintinadam.io/mcp

tools: get_experience · search_background · get_coding_stats · get_skills · get_availability · get_site_status — all read-only

LET'S CONNECT

Let's build something worth keeping.

I'm based in Osaka and work remotely with teams around the world. If you're building something interesting, I want to hear about it.