The story of Brian
Math, science, swimming, and a collaborative MIT culture
Brian grew up in suburban Maryland with an early interest in math, science, and puzzles. A public elementary program taught part of each day in Chinese, including mathematics, which made analytical work feel exploratory rather than routine. He also swam, and the repeated demand of cold early practices taught him to do difficult work even when competition itself was not his primary motivation.
At MIT he combined computer science with finance. The school surprised him by feeling more collaborative and merit based than the competitive academic culture he had expected. That environment strengthened his interest in markets, technical systems, and the question of why people make particular financial decisions.
Leaving Wall Street for more technical work
An internship led Brian to a bond trading job at Morgan Stanley. He arrived early, worked long days, and quickly found that the role did not provide the intellectual stimulation he wanted. After ten months, he left for a high-frequency trading firm where he could write code and work across stocks, futures, commodities, foreign exchange, and some crypto.
The move gave him a direct comparison between a large financial institution and a smaller technical organization. It also exposed how many intermediaries, approvals, fees, and operational layers sit inside traditional finance. Rather than treating the short Wall Street tenure as wasted time, he used it to understand which systems he wanted to replace and what kind of team culture he preferred.
Building self-custodied investment tools at Glider
Brian now builds Glider with a co-founder whose product and marketing strengths complement his technical and financial background. Glider lets users create baskets of onchain assets that can rebalance automatically without leaving the user's custody. The product removes much of the language and friction around gas, bridging, signing, and custody, while its application programming interface lets other products embed portfolios and strategies.
Brian describes the underlying building blocks as a reusable language for decentralized finance. He is also cautious about AI trading claims. The team has tested systems that can assemble diversified portfolios, but he distinguishes useful assistance from reliable autonomous action and does not present current language models as consistently capable of managing complex strategies without oversight.