infrastructure architect · ai builder
I architect the infrastructure that runs trading systems — and I build AI with the same discipline.
I'm Darrell Westbury — a platform & trading-systems architect, and a lifelong builder of things that have to actually work. The AI systems here share one rule: the model is a co-pilot, never the autopilot — it reasons, proposes, and drafts, while deterministic gates make the decisions you can't take back. The rest of it — the hardware, the hand-built synths — shares a different rule: finish what you start. Here's some of that work.
// selected work
Case studies
PreDEX
Agentic AI · 2026
A DEX perps-trading platform whose Claude-powered co-pilot proposes, scouts, and critiques across the whole strategy lifecycle — but can never execute a trade.
PredMark
Agentic AI · 2026
Monitors ~101K Kalshi and Polymarket markets for edge, trades through deterministic gates, and uses Claude for research and measurement — but never for the trade decision itself.
Luna
In-App Agent · 2026
A complete, end-to-end commerce platform for a growing hand-crafted jewelry business — products down to their components, suppliers, and multi-channel sales — fronted by Luna, an AI that takes the toil out of running it.
// lab
Experiments & fun
Where I keep the saw sharp — edge AI, computer vision, and the hand-soldered hardware habit I've had since a Commodore 64 rewired my life at 14. One of these builds is where my whole LLM itch started.
CubeLab
A photoreal, GPU-accelerated 3D cube with standard notation, mouse and keyboard controls, repeatable scrambles for timing your solves, and a near-optimal Kociemba solver that finishes it for you when you're stuck.
LLM on a Raspberry Pi
A fully dockerized, open-source path to a local LLM on a Raspberry Pi 5 + Hailo NPU — Ollama under the hood, a clean Open WebUI on top, and a genuinely capable assistant running entirely on-device.
Vision Pipeline
A self-hosted, real-time vision pipeline: one camera feed, fanned out over a Redis bus to as many models as you like — YOLO boxes and a chat-driven Moondream VLM today, a perception foundation for future robotics. Runs on Apple Silicon or a Jetson.
Ambika
A scratch-built, fully-loaded Ambika — six analog voicecards in two filter flavors, seven hand-flashed AVRs, a custom wood-cheeked case, and a 700-patch library. A deep DIY build of Emilie Gillet's (Mutable Instruments) design.
EVO64
A premium, surface-mount C64 mainboard that folds the community's best mods into one integrated board — supported by a GPT-4o Discord assistant that answers builders' questions 24/7.
MB-6582
A hand-built MidiBox SID synth: eight rare MOS SID chips, four PIC microcontrollers, a custom control surface, and a lot of soldering. A deep DIY build of Wilba's MB-6582 on Thorsten Klose's MidiBox platform.
// about
For two decades I've built and led the infrastructure behind electronic trading — global, low-latency systems where a bad deploy isn't an option. Most recently as Global Head of Trading Systems Infrastructure & DevOps at FalconX, where I designed a greenfield AWS trading platform that cleared $2B+ in notional flow within six months of launch. Before that I ran global electronic-trading infrastructure at Credit Suisse (leading a team of 75) and architected ultra-low-latency platforms for multi-billion-dollar fintech and crypto startups.
That work now includes AI. At FalconX I built an in-house AI assistant — a tools router over custom MCP servers and hand-calibrated retrieval — that helps operate a global fleet of trading systems. So the AI I build on the side isn't a departure; it's the same instinct.
The hard part of shipping AI isn't the prompt — it's the pipeline, the guardrails, the observability, and the trust. I build AI the way I build trading infrastructure: idempotent, audited, and engineered to fail safe — the model as co-pilot, never the autopilot.
Away from work, I've never stopped being a maker. The lab above runs from hand-built analog synthesizers to a ground-up reimagining of the Commodore 64 — the machine that started all of this when I was 14. Different scale, same instinct: pick something worth doing, get it all the way to done, and bring other people along for the ride.
what I reach for
- ▸ TypeScript · Python · Go
- ▸ Next.js · FastAPI · Postgres
- ▸ Anthropic · OpenAI · pgvector
- ▸ Railway · Docker · CI/CD
// contact
Building something where the details matter?
I'd love to hear about it — whether it's a role, a collaboration, or just to compare notes.
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