The Lab

Research requires a reliable environment. This page documents the hardware infrastructure and software stack I rely on to conduct , deploy , and maintain my .

🖥️ Workstations & Compute

My setup is split between mobile efficiency for academic writing and a fixed station for heavier computation.

Daily Driver

Apple MacBook Pro 13"

  • Specs: , 16GB RAM.
  • OS: macOS Sonoma.
  • Usage: Academic writing (LaTeX), lightweight coding, and remote server management.

Compute & CUDA Station

Custom Build PC

  • Specs: Intel Core i5-10400F, 16GB DDR4.
  • GPU: .
  • OS: Windows 11 (WSL2).
  • Usage: Local Deep Learning training, gaming, and heavy compilation tasks.

Peripherals

I rely on a specific set of tools to maintain flow during deep work sessions.

  • Audio: Apple AirPods Pro — Essential for deep work sessions and noise cancellation.
  • Input: (Keyboard) & Logitech G203 (Mouse).
  • Tablets: iPad Air — Dedicated to reading papers and handwriting mathematical proofs.
  • Stylus: Apple Pencil — Essential for annotations and mathematical notation.

🛠️ Development Ecosystem

I prioritize tools that offer AI-integration and strong type-checking.

IDEs & Editors

  • VS Code — For general-purpose scripting and remote SSH development.
  • Positron — Lightweight IDE for R and statistical analysis, offering superior performance to RStudio while maintaining VS Code familiarity.
  • JetBrains — PyCharm & DataGrip are unrivaled for complex refactoring and database management.
  • Theme: Catppuccin Latte (Light) / Macchiato (Dark).
  • Font: GitHub Monaspace Neon (primary, ligatures enabled) & JetBrains Mono.
main.py
 def main():
     print("Hello, Research Lab!")

Terminal & System

  • Ghostty — A fast, native, and GPU-accelerated terminal emulator.
  • Zsh — My default shell, optimized for speed and interactivity.
  • Starship — The minimal, blazing-fast, and infinitely customizable prompt.
  • Raycast — Replaces Spotlight. I use it for script commands, window management, and quick calculations.
  • Firefox — Chosen for its privacy features and robust DevTools.

🏠 Infrastructure & Homelab

To bridge the gap between theory and MLOps, I maintain a self-hosted cluster. This allows me to experiment with distributed systems, data pipelines, and network security in a controlled environment.

Hardware Infrastructure

Compute Node

Beelink EQR6

Runs my containerized workloads and Docker services.

Storage Node

UGREEN NASync DXP4800

Centralized Data Lake for datasets and backups.

Network

TP-Link Switch & Tailscale

Ensures fast, stable local communication.

Service Stack

I run these services using Docker and Portainer, strictly behind a Traefik reverse proxy.

  • DevOps & Infra — Traefik, Portainer, Gitea.
  • Databases — PostgreSQL, Redis.
  • Storage & Media — Minio (S3), Immich.
  • Security — Cloudflare Tunnels, AdGuard Home, Vaultwarden.
  • Observability — Uptime Kuma, Beszel.
  • Utilities — BentoPDF, Palmr, Home Assistant.

This list is constantly updated as I experiment with new tools and equipment.