Hank Huang · solo builder · proof of work

I build AI systems that run themselves.

Autonomous multi-agent infrastructure — and I use it to ship real things: quantitative trading, large-scale node fleets, and a token airdrop earned on-chain through real usage. Not a pitch deck.

Earned roughly 0.9% of Lighter's entire inaugural airdrop through real on-platform trading, and operate a self-healing AI agent fleet running 24/7 that I engineered end to end.

What I've built

Proof of work · on-chain earned, dry-run and live tagged

01

Earned ~0.9% of Lighter's entire inaugural airdrop — on-chain, through real usage

Earned through measured on-platform trading — not claimed, not vested, not a résumé line. The initial airdrop was distributed directly to qualifying wallets based on real activity.

The hard part is that it can't be gamed or narrated. It's on-chain — either the wallet earned the tokens or it didn't. No USD figure or raw token count shown here by design; the allocation is on-chain verifiable, evidence available on request.

earned · on-chain verifiable
~0.9%

of Lighter's entire inaugural airdrop — distributed on-chain to qualifying wallets, no claim, no vesting.

02

AI-first since high school — solo-built large-scale node infrastructure

When Nillion launched I taught myself Docker from scratch with AI — I'd never touched it — and stood up a verifier fleet solo, on a self-run fleet of ~200 Macs I operated myself.

5,000+

node verifier fleet, solo-operated — Docker self-taught with AI, from zero. Same ~200-Mac fleet also contributed compute to decentralized networks (io.net).

Context — crypto educator / airdrop-tutorial content.

03

Founded a company at 19 — a real legal entity, dual TW / HK

Incorporated a real company as the legal vehicle for this work, and set up the full operating stack myself: domain, corporate email, GitHub org, daily automation.

19

Founded dual Taiwan + Hong Kong entities as a teenager — the boring-but-real part, done properly.

墨克數位科技股份有限公司 · MKL Digital Technology Co., Ltd. · reg. 62201452, approved 2026-04-23. HK: Merkle Digital Limited.
verifiable · TW company registry

04

Web3 automation at scale — isolated infrastructure, large fleets

Large-scale, isolated node and orchestration infrastructure across multiple Web3 protocols. I designed the isolation architecture, the state machines, and the batch orchestration.

Making hundreds of isolated environments behave deterministically — the failure modes multiply with scale, and one bad assumption cascades across the whole fleet.

8-module cross-protocol automation framework (Eclipse), spanning multiple DeFi protocols — all on isolated, independently-orchestrated infrastructure.
historical operations

05

Hermes — a self-healing AI agent fleet

A 24/7 autonomous multi-agent system across 5 machines, engineered to detect its own failures and recover with no human in the loop. I engineered the system — I did not train the model.

40+

production incidents hardened into automated recovery. One silent failure — an event loop frozen for hours — turned into ~100-second automatic recovery.

LIVE · running now

06

Quant trading systems, built on the fleet

A cross-exchange latency-arbitrage engine in Go — strategy, signal reverse-engineering, and execution engine, all solo, all built on my own Hermes fleet. The strategy came from reading publicly observable fill data, not a textbook.

The edge comes from zero-fee pairs and rebate tiers, not raw spread. No capital figures, P&L, or returns published — qualitative by design.

DRY-RUN
79%

win rate · +1.83 bps per trade · 813 trades, dry-run validated.

Origin

How I got here

I went to 師大附中, one of Taiwan's top high schools. I taught myself most of the coursework using AI — not as a study aid on the side, but as the way I learned. I took a gap year and didn't finish with a diploma: two credits short, and I decided re-taking classes to hang on to a piece of paper was a waste of time. So I hold a certificate of studies, not a graduation certificate.

That gap year is when the real building started. I was already in crypto and trading, building large-scale automation solo. As capable coding models arrived, AI became how I taught myself everything I didn't already know — I picked up Docker from scratch and stood up a 5,000-node verifier fleet on a self-run fleet of ~200 Macs.

Then I got into NTHU (National Tsing Hua University) through the 拾穗 program — an admissions track that doesn't look at standardized test scores. It looks at your work, and interviews you.

The work outweighs the credential. I'm the living proof of it.

Everything since — the agent fleet, the quant systems, the company — is the same person doing the same thing I was doing in high school: using AI to build whatever I want, and going all-in on it.

The Academy

What I want to do at the Academy

My frame is AI-first, crypto-as-application. AI-native building is who I am; crypto is where I prove that AI lets one person build anything. That's the exact bet a16z is making — and the one I've been living since high school.

Coinbase is the partner I'm most built for. Crypto-as-application isn't a thesis I want to explore — it's the one I've been executing, with on-chain proof to show for it, not a pitch deck. And with NVIDIA and the $50k of compute, my whole edge is turning raw compute into autonomous systems that build and run themselves. Give me more of it and I'll show you what one builder can do.

And I'm all-in. If I'm accepted, I leave NTHU and commit to this full-time. No hedging, no deferring in case it doesn't work out. I've already bet my path on work over credentials once — this is me doing it again, on purpose.