things i built
i learn by building systems that have to make sense of imperfect, real-world input.
- ona is my Object Navigation Assistant, turning camera input into object, depth, face, OCR, and scene state. demo
- avi is my local-first visual and conversational assistant, combining on-device vision and wake detection with natural voice interaction. demo
- friendnet is a custom vision model for friendly-face classification, compared against ResNet and CLIP baselines. demo
- copnow taught me product gravity. i co-founded the marketplace and led ML/backend work around trust, commerce, analytics, and APIs. demo
- orchestra is a Rust/macOS synchronization daemon for AI coding workspaces: deterministic templates, atomic writes, and drift detection.
i’m interested in computer vision, applied ml, and backend systems. if you’re building something ambitious in that space, let’s talk.
machine learning
computer vision keeps pulling me in: scenes, objects, faces, products, and the meaning hidden inside a frame. i want to build perception systems people can trust under messy lighting, partial views, and real-world uncertainty.
during my co-op at the Ontario Public Service, i built AWS Bedrock RAG and LLM workflows that reduced analyst triage time by 35%. i also completed Fanshawe’s Artificial Intelligence co-op program with a 4.2 GPA.
before that
before ml became the center, i was mostly a backend engineer. at SHIIP, i built high-throughput Go and Python services for logistics.
earlier, i worked on practical web systems at Curacel and TEEK-TECH.
my computer science thesis was a sensor-guided autonomous vehicle. that project still explains me: software meeting the physical world.
writing
An accessible exploration of pixels, human perception, computer vision, and how machines transform numerical image data into an understanding of the world.