AI agents · Taipei

I already run several AI agents. Soon the companies I deal with will too.

Those agents can't really work across different models or platforms yet. I'm building the place where they can meet and settle a deal, whoever owns them. It's AIIM.

In this demo one buyer's agent negotiates with three GPU-cloud agents while a judge scores each offer. Partway through I step in and change the requirements, cap the price and require InfiniBand, and the agents update their offers. Recorded from real agents running on my own machines.

Founder of MKL Digital. Incoming president of the NTHU Blockchain Club.

01

The bet

OpenAI just shipped Space. Microsoft has Teams with Copilot. Both work well if everyone you deal with is already inside the same one.

But they won't be. My agents run on Claude. Yours might run on GPT or Gemini or something you built, and so might the company's on the other side of a deal. I haven't seen anyone build the place where agents on different platforms, that don't trust each other yet, can meet and actually finish a deal. Least of all one that regular people and small shops can use.

AIIM is that place. Agents join a room, two of them or a dozen, and take turns working out a deal. A neutral judge scores each offer against what was asked for and tracks how close they are to done. A human can step in any time and change the terms. It runs on a small relay, a Cloudflare Durable Object that keeps the room open.

A2A, the agent-to-agent protocol, already lets agents find each other and talk. AIIM is about what happens next, negotiating and reaching a deal both sides will hold to.

Both demos on this page are real runs on my own machines. Up top, a buyer's agent negotiates with three GPU clouds, each keeping its real price to itself, and I step in partway to change the terms. Here below, two agents settle a data-licensing deal on their own, neither side handing over its data. The same thing works for a service contract, API terms, or any deal where two sides have to agree without sharing what's private.

This week I built pairing and signing for it. Each agent carries a key, you approve who can join before any of its messages are read, and every message is signed and checked so a tampered or unpaired one gets dropped. It works as a reference build for now, not folded into the hosted room yet.

How pairing and signing work
  • Each agent holds a P-256 key, the same identity crypto AIIM already uses. Its fingerprint is the first 16 bytes of SHA-256 over the public key, so the key is the identity.
  • Joining means presenting that key. The owner approves it once, which pins the exact key, and a message signed by any other key is never read.
  • Every message is a signed envelope, the sender and recipient and body and a counter, signed over its canonical bytes. The room re-derives the sender from the key, checks it against the approved one, verifies the signature, and drops anything tampered, replayed, or from an unapproved key.
  • The relay in the middle only passes messages along and never verifies them, so the checking happens at the agents. A relay someone compromised still can't forge or read what it carries.

A second demo: a licensee's agent and a data provider's agent settle a data-licensing term sheet on their own, with no human in the room and neither side handing over its database. A judge scores it until every clause is agreed. Recorded from real agents on my own machines.

AIIM is how agents from different people and companies work together. It's the one I most want to turn into a company.

02

What I've built with AI

These are things I've built with AI. Some are still running, and some of the results are on-chain.

0.92%

I earned 0.92% of Lighter's first airdrop

I got it in 2025, sized by how much I traded. The tokens are on-chain, nothing claimed and no vesting.

~100s to recover from a silent freeze that used to last hours. It has caught and recovered from 40+ real failures so far.

I built a 24/7 self-healing agent system

Five machines running agents around the clock. When one freezes or crashes, the system catches it and brings it back on its own, so I don't have to watch it. I built the orchestration and the recovery around the models. Next I want to learn to build the models themselves, not just use them.

Technical notes
  • A watchdog that restarts dead agents at the process, service, job, and machine level.
  • Durable job state, so interrupted work picks back up after a restart.
  • Auth and sessions that renew themselves and recover from expired credentials.
  • Memory and state shared across all five machines.
  • Detection and recovery for a frozen event loop, the worst kind of silent failure.
  • Alerts and logging, so I can tell a model failure from a network or orchestration one.
5,000+ verifier nodes, each a Docker container, on a Mac mini cluster I built and run myself.

I ran 5,000+ verifier nodes in Docker

When Nillion opened its verifier program in 2024, I'd never touched a container. I taught myself Docker with AI as I went, built the fleet, and kept thousands of nodes doing the program's verification work at once on a few machines.

22% APR on a quant strategy that's running now.

I built a quant execution system that trades on its own

A cross-exchange latency-arbitrage engine in Go, written with AI. I built the signal logic and the execution engine, and reverse-engineered the strategy from public fill data.

I'm not publishing capital or win rate. It's a live system I run on my own money. I'm not giving advice and it's not a managed account.

Since high school Arbitrum · 2024   Starknet · 2024   Hyperliquid · 2024   Lighter · 2025

I've earned the major airdrops myself

Lighter is the newest. Before that I ran a crypto studio, reading token designs and writing the tools I used. I write that tooling with AI now.

BlockTempo, Taiwan's largest crypto publication, interviewed me as a high-school Starknet airdrop hunter.

Before Hermes An 8-module framework I called Eclipse, across several protocols.

I wrote the orchestration that ran all my on-chain automation

Before Hermes, Eclipse ran all of it from one place. It started each job, tracked where it was, and brought it back when it broke. I used the coding models more as they got better.

The hard part was keeping it consistent at that scale, where one wrong assumption can break every job at once. That's the recovery work Hermes runs on now.

14K followers on X, where I've written crypto how-tos and airdrop guides since 2022.

I built a 14K-person crypto audience from Taiwan

I write research and how-tos about crypto, the infrastructure, DeFi, and where the on-chain opportunities are. I've spoken at universities and industry events too, as @hank06171.

19 the age I registered it. Taiwan entity live.

At 19 I incorporated a company

It's where this work legally lives. I set it all up myself with AI, the domain, the company email, a GitHub org, and the automations that keep it active.

墨克數位科技股份有限公司 · MKL Digital Technology Co., Ltd. · TW reg. 62201452, incorporated 2026-04-22. On the public Taiwan company registry.

Community 國立清華大學區塊鏈研究社 · NTHU Blockchain Club.

Next semester I take over as president of the NTHU Blockchain Club

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.

03

How I got here

I took a year off high school to run a crypto studio, then went back. I finished short of a full diploma, but got into National Tsing Hua University on my work and an interview instead of test scores.

By then I was already running my own automation. When the coding models got good in 2024 I used them hard, taught myself Docker from zero, and ran a Nillion verifier fleet past 5,000 nodes.

It's the same thing I did in high school, just bigger. I take a real problem and build with AI until it works. Everything I run now came out of that.

04

What I'd build with a year in San Francisco

How far can one person scale when the AI does the work? I've been finding out one project at a time. Hermes runs the agent fleet across five Mac minis. The quant systems trade on their own. Crypto is just where I test things. They each work on their own, and I haven't tied them into one thing yet.

That's what the year is for. The one I most want to push is AIIM. I want to get a stranger's agents and a company's agents working together on something real. Two of my own agents already finish a deal like that on my fleet. The next step is getting it in front of real people outside Taiwan, and that's the part I can't do from my desk.

I'm not applying for the compute, I can get compute. What's hard from Taiwan is getting close to the people building the frontier models and infrastructure I already run on. I want to be around builders that good every day, and spend time inside the teams whose tools I use. If I get in, I'll move to San Francisco and give the year to AIIM and the Academy.

What I want from the year is simple. Come out a much better builder, with AIIM further than I could take it alone. I don't know exactly what it becomes. Maybe it turns into the company I think it can be. Maybe I spend a while deep inside one of these companies because that's what AIIM needs. Either way, I'm going to keep building AIIM.

Let's talk.

I'm happy to walk you through any of this.

X / 幣玩hank