Victor Hong

AI can build almost anything now — and most businesses are still thinking too small about what that means. The value is in reframing what's possible, then knowing what done actually looks like. That's what 25+ years teaches you.

Sydney → Beijing → Vancouver → Sydney — I took the long way home. Now based back in Sydney, I work with business owners and decision-makers who need someone to cut through the noise — whether that's cloud infrastructure, security, or figuring out where AI actually fits into their operations. No jargon, no upselling. Just honest, practical advice.

My clients have spanned accounting, legal, medical, manufacturing, mechanical engineering, fashion, finance, retail, VFX, and geosciences — across Australia and Canada. That variety means I've seen a lot of different setups and challenges, which helps me spot what works and what doesn't pretty quickly.

These days my focus has shifted to where IT and AI intersect — helping businesses figure out which workflows actually benefit from automation, and which are just hype. I evaluate AI output the same way I'd evaluate a junior hire's work — does it actually solve the problem, or does it just sound like it does? That judgment call is the hard part, and it's where 25 years of seeing what actually works in production matters. I'm still across the full cloud stack (AWS, Azure, GCP, M365), but increasingly the conversations I'm having are about AI integration, not just infrastructure.

Day to day, I live in Claude Code (since its launch in February 2025), Obsidian as a second brain, and Daniel Miessler's LifeOS project (formerly PAI). What started as tinkering with OpenClaw (since January 2026, back when it was still called Clawdbot) and NousResearch's Hermes Agent (since v0.14.0) is now production infrastructure — I run both inside real companies today, on real workflows. The two bets I'm making hardest right now: the shared company brain — after evaluating the field I've settled on gbrain, from Y Combinator's Garry Tan, and client pilots are underway now — and Buzz (buzz.xyz), Block's new open-source workspace where your people and your AI agents share the same channels, which I self-host, run my own agents on, and am readying for client engagements. Both are early and both are open source: gbrain keeps the knowledge itself in plain, git-backed files you own outright, and Buzz is self-hosted on an open protocol, so nobody can lock you out of your own workspace. Swap the model, the harness, or the workspace — the knowledge stays yours. Everyone I watch actually using these tools in business is converging on the same two needs: stop knowledge living in silos, and hold it in a form no vendor can lock up. If you're working with any of these — always up for a chat.