Web Developer, Full-stack Developer, Data Analyst, Product Engineer, Infra Engineer, SAR / Satellite Engineer, AI-native Builder
A developer who uses AI as a thinking partner — expanding role boundaries while building systems alongside it.
I like absorbing a company's problem quickly from the sidelines and closing the whole loop — from domain learning through full-stack implementation to operations.
I cover frontend + backend + infra/CI-CD + AI end-to-end as one full-stack developer, and have extended that reach into the SAR / satellite domain I first met at work.
Stage diagnosis starts from your main role
The same SAR work means something different when a geospatial-engineering major does it as their main role versus when a web developer does it as a domain expansion. The matrix below and the 5-stage model anchor my current coordinates.
AI usage · 5 stages — in my own words
The stages aren't sequential steps. Once you flip into thinking-partner mode — set the context → let AI ask you questions → think again → give feedback → and watch your own thinking sharpen in the loop — stages 3, 4, and 5 unlock together.
Same ingredients. Different course.
An assembled resume — the same 6 projects read differently for different audiences. Use the track toggle below to switch the depth and angle to the company / domain context.
Lumir SAR Data Platform
Unified search, storage, analysis, and request for Sentinel + LumirX — an internal satellite-data full-stack service (3 layers)
There was no integrated service to take Sentinel and LumirX data from search through storage, analysis, and request in one cycle. Storage (NAS + CDSE), InSAR analysis (SNAP · ISCE2 · MintPy), and the user-facing frontend were all separate — making it hard for a user to get from 'I want this location' to a finished result.
I'm solo-designing and building the 3-layer integrated service. **Storage layer** (sar-data-retrieval, NestJS monorepo + CDSE + NAS PoC + DDD 5-layer) + **Analysis layer** (lumir-linux-snap, 5-tool stack + multi-agent worktrees) + **Frontend layer** (sar-search-and-analyzer, Next.js + map + AOI + analysis-request UI). All three are 100% AI-native, and I'm back-transferring the 4-layer + CQRS + Korean-method-name pattern validated in him into the company backend design.
With ISCE2 in place, the analysis layer is fast enough that *weather- and season-independent surface-displacement data as a service* is now feasible. The key point: a user picking a location on the map gets either the stored result instantly, or a freshly-processed one — and the whole pipeline is full-stack-owned by a single person.
SDPE — SAR processing pipeline orchestration
Lumir LumirX multi-stage SAR pipeline · NestJS with 5 subsystems + DAG
There was no system letting an operator configure, run, trace, and recover the L0–L3 LumirX raw-SAR pipeline, and adding new satellites or algorithms needed to cost as little code change as possible. From my side, I was dropped into the pipeline domain blank-slate.
On top of an inherited NestJS 5-subsystem monorepo, I designed and built the DAG planning UI (no Figma — the UI code plus Playwright e2e *is* the planning document). Built the GitLab CI/CD from zero, did the detailed design for interfaces/csc-8, fed the 80–100-page ICD/SAD docx directly into AI, ran a tight loop of small 'does this break the rule?' reviews, and wired up an auto-redeploy hook that fires when ops-console work completes.
Despite entering blank-slate, I produced detailed design and implementation on top of my senior's base. The architecture has settled into a shape where adding Sentinel support or a Snappy-based DAG step takes near-zero time.
him (home-inventory-manager)
100% AI-native full-stack side project
Real household-inventory pain points (where did it go · expiration dates · stock levels · re-order alerts), plus a personal motive: prove I can solo every layer — UI planning, frontend, backend, infra.
NestJS + CQRS + TypeORM + PostgreSQL + a frontend + Docker Compose + S3 + Terraform IaC + backup metrics → Grafana. Architecture patterns are lifted directly from work-validated ones. 100% AI-native: AI writes code; the three human nodes are (1) UI planning, (2) code-connection review, and (3) e2e tests. Building self-hosted infra so DB-level user isolation works.
Personal use + 1 beta user (a shared hub), 13+ wiki assets fed back (the 2-stage design process, the 5-layer testing setup, 31 consistency-gap repairs, automated backup metrics, and recipes for both sides of S3 + Terraform). Back-transfer into work happened too — the him UI + current e2e test pattern is being adopted to strengthen the company frontend, and the 4-layer pattern is being applied to the sar-search-and-analyzer backend design.
Lumir-ERP (internal back office)
Four domain workstreams together — full-stack CMS + frontend for resource scheduling, LRIM, and interview management
We needed full-stack delivery of four internal back-office domains (resource scheduling · CMS · LRIM hiring · LRIM interview management). It's the company's broadest-user project — every employee, plus recruiters, interviewers, evaluators, and external applicants.
Next.js 14/15 (App Router) + TypeScript + Tailwind + shadcn/ui + SWR + Playwright. A Plan (mock) / Current (real API) environment-split pattern (inherited from the team lead) + per-domain Context + (cms)/(sms)/(ams)/(uam) domain separation + UAM uses MongoDB directly. The two LRIM apps form a pnpm + Turborepo monorepo (@repo/ui · common · modules). 100% AI-native: AI writes code; I review the UI planning, code connections, and own the e2e tests.
A 4-domain back office running in production for every employee plus external applicants. CMS is solo full-stack (planning · backend · tests; frontend tests are being improved). For the other three, I own all frontend functionality. The real asset isn't 'four projects' — it's the ability to adapt to four domains in parallel and the full-cycle solo experience on CMS.
Brain Trinity
Systematizing cognitive load distribution + a domain-adaptation mechanism
To keep my biological brain on the important work, the 'important but doesn't need to live in working memory' content had to be offloaded to an external system. Plain note apps are searchable but don't synthesize, link, or re-use (trigger: Karpathy's LLM Wiki video).
Karpathy-style LLM Wiki 3-layer (immutable raw/ + AI-compiled wiki/ + derived Output/) + Claude Code as the only collaborator + a skill system (ingest/lint/query) + a frontmatter schema + auto-updating index/log + Obsidian graph + auto-syncing MEMORY. 100% AI-written; the user's input is just prompt chat.
56+ wiki pages and counting; every ingest triggers automatic cross-linking, raw-frontmatter sync, and index/log updates. This resume, the self-diagnosis, and the 6 project briefs are themselves the living evidence of Brain Trinity's stage-4 automatic operation. Bundling internal projects (sdpe + lumir-sar-platform) on top would open a stage-5 in-house systemization flow.
Main role · Learned domain · Future
I separate my main role (web development) from the learned domain (SAR · satellite imagery) picked up at work. Depth-within-role and role-expansion are two distinct things.
Main role · Web Development
- TypeScript
- Next.js
- React
- Tailwind CSS
- Playwright (e2e)
- NestJS
- CQRS· @nestjs/cqrs
- TypeORM
- PostgreSQL
- pgmq· PostgreSQL message queue
- DDD 5-layer· domain / business / context / handlers / interfaces
- Jest + Testcontainers
- Docker · docker-compose
- GitLab CI/CD· Built from scratch + custom mail
- Terraform IaC· Recipes for both S3 sides
- Grafana + Prometheus· Backup metrics via textfile collector
- FastAPI· Analysis server (bridged to NestJS)
- Claude Code· Primary thinking partner · 100% AI native
- 다중 agent 워크트리· Agents 1–4 in parallel + handoff system
- Brain Trinity 위키· Karpathy /raw pattern + skill system
Learned domain · SAR · Satellite imagery
- Sentinel-1 SAR
- ESA SNAP 12· MicrowaveTBX (SAR)
- SNAPHU· Phase unwrapping
- MintPy· SBAS time series
- ISCE2· New track — speed unlocked
- StaMPS PSI· Octave + 12 patches
- DInSAR · SBAS · PSInSAR
- PyAPS + ERA5· Atmospheric correction
- CDSE· Copernicus Data Space
- QGIS
- Python (분석)· rasterio · geopandas · shapely
- Snappy· SNAP Python bridge
Changuk Woo
Web Developer, expanding into SAR. I cover frontend + backend + infra/CI-CD + AI end-to-end as one full-stack developer, and have extended that reach into the SAR / satellite domain I first met at work.
Are you launching into a new industry that demands fast domain absorption?
Do you need a full-stack engineer who treats AI as a collaborator, not just a tool?
Looking for someone who owns one full cycle — code, planning, infra, and testing?
This portfolio, the self-diagnosis, and all 6 project briefs were compiled from a meta system called Brain Trinity. It uses the Karpathy LLM Wiki pattern + Claude Code collaboration + a skill system, and currently holds 56+ accumulated wiki pages.
Given an unfamiliar domain, I believe structured information + an AI-native foundation can solve almost anything. Brain Trinity is both the methodology and the asset — and I can demo it live in an interview.
- →Currently extending into embedded systems
- →Expecting a natural extension into hardware and robotics
- →Plan to grow Brain Trinity into a complete personal system (voice + journal + meeting notes + PDFs unified)