I Like Tech

As a child, my dad encouraged me to tear apart old computers, learn about the components, and understand how software interacted with hardware to accomplish amazing things. As a result, I started building computers at a very young age because the logical nature of computing was incredibly appealing. Around the same time, I grew fond of how cars work. I learned about the different parts of an internal combustion engine, how they interact, and how to troubleshoot problems. The real springboard for my insatiable love of technology was learning about the Internet’s architecture, protocols, DNS, servers, and building websites with PHP, HTML, and JavaScript in my early-to-mid teens. After that, I branched out, taking on freelance projects and code contributions, dabbled in Java/Android programming, and learned how to apply my growing tech knowledge/skills to my career in healthcare. In the process, I also developed a free iOS ultrasonography app called EchoTools (later renamed EchoRef) during my fellowship and expanded on this by creating a suite of other free reference iOS apps (learn more at https://rkref.app).

Meet The Gear

Workstation

Apple Mac Studio M3 Ultra

Notebook

Apple MacBook Pro 16″ M4 Max

Tablet

Apple iPad Pro 11″ with Magic Keyboard

Battlestation

Project Frost – Custom Gaming PC Build

Homelab

AI, Storage, Docker, Automations

Rides

Porsche Taycan Turbo S &
Tesla Model S Plaid

Phone

Apple iPhone 17 Pro Max

Cans

Apple AirPods Max USB-C

Lens

DJI Mini 4 Pro Drone &
Osmo Pocket 3

Time

Apple Watch Ultra 4

Racing Simulator

I like fast cars.

Driving… watching GT and F1… learning about engines and efficiency, drag coefficients and gear ratios, tire compounds and braking points. The mechanical precision of racing appeals to the mathematical part of my mind. A small input to the steering wheel or brake load cell… and the system responds predictably. Too much throttle or braking, and you’ll pay for it by missing the apex or veering off track. Too little commitment, and you leave valuable lap time on the track.

Building a racing simulator felt like a natural extension of that curiosity. Not to chase lap records, but to understand feel. Load transfer under braking, how aero grip builds with speed, why a car that feels stable at turn-in with trail braking can still punish you on exit, a wheel with proper force feedback, a pedal set that demands pressure rather than travel… just so much cool stuff to consider.

CockpitSim Lab P1X-Pro
SeatSim Lab S1 Enduro with Sim Lab Sparco Seat Slider
MonitorsSim Lab Triple Monitor Mount with 3 x Samsung Odyssey 31.5″
Sim Lab Quad Monitor Stand with LG 45-inch UltraGear 5K2K Curved Gaming Monitor
Wheel BaseFanatec ClubSport DD+
Quick ReleaseFanatec Sim Racing QR2 Wheel-Side
Steering Wheels– Fanatec ClubSport Steering Wheel Formula V2.5 X
– Fanatec CSL Elite Steering Wheel Porsche Vision GT
PedalsSim Lab Pedal Slider Base with Fanatec ClubSport Pedals v3
Belt TensionerQubic QS-BT1
DashDV480 Pro V5 SimRacing Dashboard
Action TriggerElgato Stream Deck MK.2
SpeakersEdifier M60 Compact 2.0
Keyboard/MouseLogitech K400 Plus Wireless Touch
LightsGovee Floor Lamp 2 x 2, Aqara T1 Light Strip

Here are the components for the PC I built to power the simulator. It started with the Vulcan small-form-factor core, with some significant upgrades.

NameSim Racing PC
OSMicrosoft Windows 11 Professional
MotherboardASUS ROG STRIX B760-I Gaming Wi-Fi Mini-ITX
CPUIntel® Core™ i5-14600K
MemoryCorsair Vengeance DDR5 RAM 32GB 
GPUPNY NVIDIA GeForce RTX 5070 Ti Overclocked Triple Fan 16GB GDDR7 PCIe 5.0
StorageSamsung 990 EVO Plus SSD 2TB
PSUASUS ROG Loki 1000 Watt 80 Plus Platinum SFX-L
CPU CoolerCorsair iCUE H100i RGB Elite
CaseLian Li A3 microATX Mid-Tower Case
MouseFeinmann F01 Gaming Mouse
KeyboardRazer Huntsman V3 Pro Mini 60% Esports Gaming Keyboard

RK.MD Homelab Rack

In the Batcave, I run a homelab that powers my networking, homelab infrastructure, file storage, and local AI stack. The computational backbone of this setup is built around an Apple Mac Studio M1 Ultra, GMKtec EVO-X2 AI Mini PC, and an NVIDIA DGX Spark workstation running a series of Docker containers (see below), AI-related stacks (oMLX, VLLM, TRT-LLM, LMStudio), and a Synology RS1221+ 8-bay NAS.

The heart of the build is the Sysracks 22U Wall Mount Server Rack Cabinet (24” Deep). I used Rack Studs to mount all the hardware, and I connected 6000K LED cabinet lights to a smart plug for cabinet illumination. I spray-painted the AC Infinity intake and exhaust fan plates with Rust-Oleum Metallic Aluminum to fit the Ubiquiti aesthetic.

Now, here are the components from top to bottom (rack units in parentheses).

I also 3D-printed several accessories (rack mounts, temperature sensor holders, etc.) and created dust meshes for the air intake fans.

Docker Homelab

A Docker container is a lightweight, standalone executable package that includes everything needed to run software, including the code, system tools, libraries, and dependencies. Docker provides an efficient and consistent way to package, distribute, and run applications in a portable, scalable, isolated, and reproducible manner. Best of all, these containers are free!

Containers are isolated from each other and the underlying host system, allowing applications to be deployed and run consistently across different environments. Docker containers use containerization technology to virtualize the operating system, allowing multiple containers to run on a single host machine without conflicts.