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
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.
Training is moving back onto the machines people own, and those machines are not a fleet you control. Apple GPU support sat on my plan for two years because nothing looked broken; the runs finished, they were just slower. Turning Metal on was worth 1.7x to 4.2x per epoch on the one M1 I own, and much less end to end. The measuring is what found the real bugs, three of which never raised anything: is_bf16_supported() answering True on a card that only emulates it, a gradient scaler silently dropping optimiser steps and halving a mAP, and 16-bit being slower than 32-bit on Apple Silicon even though everything works.
THE NEXT PAGE
Have a project, a question, or an interesting idea? I’d love to hear it.
