
Transhumanist HackerHere to Help
I work to interface with and enhance the human mind.
I want to help humanity reach its full potential. I see a fantastic future where we harness AI to augment ourselves, expanding our ability to build, create, and communicate together.
In 2018, I was studying software engineering in Canada. One day, while taking notes on my phone, I realized those notes weren’t something separate from me, they were actually a direct extension of my mind. They were part of my mind. That insight led me to Vannevar Bush’s Memex and a question: what could we become if we increased the bandwidth between our minds and our tools? I started building smart glasses to explore how we could enhance our memory, hearing, cognition, and communication.
That curiosity led me to work with the inventor of smart glasses at UofT, travel across North America in a mobile hacker lab I built, X-Ray LLMs with Stephen Wolfram in Illinois, design circuit boards with software at a YC startup, and eventually join the MIT Media Lab. At MIT, I worked with Pattie Maes, the inventor of proactive AI agents.
In 2024, I started Mentra to build the missing piece: an open-source smart glasses OS for a future where everyone is free to augment themselves.
FEATURED PROJECTS

MentraOS
Today, my main focus is MentraOS - the open source operating system for smart glasses. MentraOS puts you in control of your software, AI, and data to ensure a future where everyone controls their own reality. I'm Co-Founder and CEO of Mentra.
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Human Eye as a Camera Wearable Computer
A wearable brain-computer interface that can scan the visual cortex and recreate an image of whatever the subject is looking at.
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Mobilab: 40 Year Old RV Mobile Hacker Lab
A 40 year old RV converted into a mobile hacker lab for wearable computing adventures across North America.
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Extracting Linguistic Relations from Word Embeddings & LLMs
(king – man + woman) == (queen) | But what else is there to discover in semantic latent space? We explored the high dimensional space of word embeddings and large language models (LLMs) to discover how semantic meaning is represented in vector space in a human-understandable way.
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