MACHINE LEARNING LEAD / AI ARCHITECT
I’m Viet Anh. I lead machine learning at Scopic and created AnyLabeling.
From the first architectural decision to the system people rely on — I work where hands-on engineering meets technical leadership. I build in the open and share what the real work teaches me.
01 / SELECTED WORK
OPEN SOURCE · COMPUTER VISION
Less time labeling.
More time building.
02 / TECHNICAL LEADERSHIP
My approach to leading AI work: stay close to the problem, make the tradeoffs visible, and give people a system they can understand.
Background & experiencePrivacy, latency, cost, and the people using the system belong in the architecture from day one.
Benchmark the options. Document the tradeoffs. Build confidence through evidence, not a polished demo.
Share the reasoning, write down the lessons, and make the next engineer’s work easier.
03 / CURRENT EXPLORATIONS
04 / FIELD NOTES
Lessons from LiteLLM, Railway and ConnectWise on credential recovery and tenant investigation, alongside the Node.js Brotli fix and checking the runtime actually deployed.
In two weeks, one small team used coding agents to publish open-source reimplementations of Photoshop, Premiere, Acrobat, Word and Excel. I read the repositories to see how they were built, how far along they really are, and what commercial users still depend on. My conclusion: version one of software is becoming cheap, and the valuable work moves to fit, verification, support and risk.
Writing software helped shape how we think as engineers. As AI takes over more implementation, I worry about discovery debt: the questions and insights we miss when we skip the work that might have revealed them. Here is how I want to preserve understanding, curiosity, and judgment while working with coding agents.
THE NEXT PAGE
Have a project, a question, or an interesting idea? I’d love to hear it.
