AI video analytics at Playtag
mono_xyz turns everyday footage into person-level behavioral insight; I work on it full-stack, across product, backend, and infrastructure.
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=--::::::..::....:**#**#*****#########*****************##########*************=...:.:---About Brandon
I came from film, stayed for systems, and now build toward AI infrastructure.
Full-stack AI engineer at Playtag in Seoul, working on mono_xyz, AI video analytics for human behavior, across product, backend, and infrastructure.
- Korean / English
- Seoul
- calendar events synced at MOBA
- 7M
- MOBA sync throughput gains
- 5-20x
- software switch from film
- 2019
I started in film and theatre, moved into software in 2019, and grew through frontend, full-stack, backend, DevOps, and AI-native engineering work.
The through-line is not a title. It is the way I work: trace the system, name the tradeoffs, verify with tests and logs, then write down what changed my mind.
career arc
- [2004-2012]
Film and theatre at Hanyang
Studied cinematography, film directing, sound, and editing before software became the craft.
- [2019-2021]
Self-study into engineering
Built a foundation through JavaScript, Java, C, Nand2Tetris, algorithms, and production-oriented web work.
- [2021-2025]
Frontend to backend ownership
Worked across MODULABS, Moviation, and Playtag, moving from product UI to full-stack delivery, service migration, and production operations.
- [2025-2026]
MOBA backend, DevOps, and AI data systems
Sole-maintained calendar sync while owning billing surfaces, real-time updates, AWS/Terraform infrastructure, and Airflow-based data pipelines.
- [2026-now]
Back at Playtag as a full-stack AI engineer
Returned to Playtag on August 31, 2026 to work on mono_xyz, AI video analytics for human behavior, across product, backend, and infrastructure.
what I build now
Infrastructure that the team can operate
Terraform, CI/CD, and AWS deployment paths that are reviewable, repeatable, and fail visibly instead of silently.
Agent-native engineering
B3, reviewer loops, and verification tooling that keep AI-assisted work auditable, from the first prompt to the merged diff.
Current operating surface
- Backend systems92%
- AWS / Terraform78%
- AI video analytics66%
- Agent workflows54%
- B3 knowledge layer42%
personal systems
Brandon's Binary BrainB3
A version-controlled personal operating system for notes, agent rules, skills, reviewer loops, and decision memory.
AI knowledge platformCrucio
A personal AI system for model experimentation, knowledge workflows, and pipeline inspection.
public learning surfacebrandonwie.dev
The place where production notes become essays after the evidence is strong enough to share.
operating principles
Correctness before cleverness
Timezones, recurrence rules, soft deletes, orphan rows, and race conditions deserve boring precision.
Evidence beats confidence
I use AI heavily, but decisions still need tests, logs, diffs, production data, and explicit ownership.
Write the tradeoff down
Good systems work is remembering why a decision was reasonable when the context is no longer fresh.
learning edge
Building toward
MLOps and AI engineering, without pretending the work is already done.
The next edge is deeper computer science and AI systems work: graduate-level foundations, production ML infrastructure, and tools that make agents easier to verify.
- Georgia Tech OMSCS target: Spring 2027
- GTx math and algorithms certificates
- AWS Developer and Solutions Architect track
- Operating systems, concurrency, and lower-level systems depth