I run a fleet of AI agents across five machines and point it at work that usually takes a team: quant trading, a few thousand crypto nodes, and a token allocation I earned by trading on the platform myself.
They admitted me on my work, not a test score.
Every claim here is tagged so you can check it yourself: live, on-chain, or historical.
On-chain proof
The tokens landed in the wallets that qualified, sized by how much I traded. Nothing to claim, no vesting.
of Lighter's first airdrop, paid on-chain to the wallets that qualified.
The bet
Soon everyone runs their own AI agent. Every company runs one too. None of them can reach each other yet. AIIM is the layer in between: your agent finds another person's, or a company's, and they finish the job together.
Five machines running agents around the clock. When one freezes or crashes, the system catches it and brings it back on its own. I built the orchestration and the recovery around the models. I didn't train a model.
production failures turned into automatic recovery. One silent freeze once ran for hours; now it clears on its own in about 100 seconds.
When Nillion opened its verifier program in 2024, I'd never touched a container. I learned Docker with AI as I went and ran the whole fleet myself.
verifier nodes in isolated Docker containers, on a Mac mini cluster I run at home.
I built the isolation between environments, the state machines that tracked each one, and the batch logic that ran them together. Hundreds of environments, all behaving the same way.
HistoricalIt's where this work legally lives. I registered the domain, set up the company email and GitHub org, and wrote the automations that keep it active.
the age I registered it. Taiwan entity live.
Community
I started as a member. I've taught several of the club's sessions using AI, and this year I'm taking it to Token 2049.
國立清華大學區塊鏈研究社 · NTHU Blockchain Club.
Quant systems
A cross-exchange latency-arbitrage engine in Go. I built the signal logic and the execution engine, and reverse-engineered the strategy from public fill data on the exchanges.
exchanges wired into one Go latency-arb engine, running live on my own fleet.
Origin
I went to 國立臺灣師範大學附屬高級中學 (The Affiliated Senior High School of National Taiwan Normal University, HSNU), one of Taiwan's top high schools. I taught myself most of the coursework with AI instead of sitting through it. I took a gap year and left two credits short of a diploma, because re-taking classes to hold onto a piece of paper wasn't worth the time. So I have a certificate of studies, not a graduation certificate.
The gap year is when I started building for real. I was already deep in crypto, running my own automation at scale. Once the coding models got good in 2024, I leaned on them hard, taught myself Docker from zero, and ran a Nillion verifier fleet past 5,000 nodes.
Then I got into 國立清華大學 (National Tsing Hua University, NTHU), one of Taiwan's top research universities, through the 拾穗計畫 (Gleaner's Project), part of NTHU's Tsing Hua Interdisciplinary Program. It ignores test scores and admits you on your work and an interview.
It's the same thing I did in high school, just bigger: point AI at something real and take it all the way. The agent fleet, the quant systems, and the company all came out of that.
The Academy
The way I work: one person running a fleet of agents, with crypto as the test. The results sit on-chain, so the record is whatever happened. I've worked like this since high school.
Of the founding partners, my work lines up with Coinbase most directly. I've built exchange systems, execution engines, and automation around on-chain incentives. I'm not here to explore crypto as an idea; I've been shipping it. I build everything on OpenAI and Anthropic models too, so I'd want to be close to those teams.
The project I'd push hardest here is AIIM. Everyone ends up with their own AI agent, companies too, and right now none of them can talk to each other. AIIM is the bridge. It already runs agent-to-agent on my fleet. To take it past a prototype I need more than five machines at home, and the people the Academy puts in the room.
The compute matters, but it's not why I'm applying. I want to be in the same room as the people building at this level, every day. I can engineer the systems myself. The network is the one thing I can't build from a desk in Taiwan.
I'm not deep in just one thing. I've got three that stack: the agent fleet I built, the allocation I earned on-chain, and the quant system running on both. And I don't only build alone. Next semester I take over my university's blockchain club, where I've been teaching people to work the way I do.
If you take me, I leave 國立清華大學 (NTHU), move to San Francisco, and spend the year on this full-time. I've bet on work over credentials before, when I walked away from my diploma. I'll do it again.