I’m Xiang Zhu, a Cloud and DevOps Engineer in Auckland. I bring 16 years of enterprise IT experience, including consulting roles at Thoughtworks and Accenture, to infrastructure design, automated delivery and systems operations.
My background spans 16 years in enterprise IT (2008–2024), including consulting, systems engineering and project delivery. My work connects cloud platforms, container orchestration, delivery automation and day-to-day operations.
At Thoughtworks (2018–2024), I worked as a Senior Consultant on infrastructure and DevOps projects for clients including Singapore’s Health Promotion Board, Daimler AG, China Merchants Bank and SadaPay. Earlier, at Accenture (2016–2017), I helped design and implement container cluster solutions for Huawei HiCloud.
I have completed a Master of Software Engineering at Yoobee Colleges in Auckland. This portfolio complements my enterprise career with recent, inspectable practice in infrastructure and cloud delivery. I use AI to support investigation and implementation, then check changes against source, tests and deployed behaviour.
I use Codex across planning, implementation, debugging and documentation. This snapshot shows the rhythm of that work; the Story Filler case study shows how I check the results.
Selected software-project workspaces
– · Auckland time
Recorded turns
303
Active days
17
Calendar days covered
24
September 2026
MTWTFSS
5678910111213141516171819202122232425262728
Turns per day01–910–2930–5960+
Static snapshot updated 28 September 2026; the final day is partial. Activity measures use of Codex, not code output or engineering quality.
What these numbers include
Aggregated locally from retained Codex metadata for explicitly selected software-project workspaces. A turn is one recorded assistant work cycle; a follow-up starts another turn. Planning and documentation in those workspaces are included. Other workspaces, including job-search tasks, are excluded.
Only top-level turns with a start time and a finished, failed or interrupted status are counted. Forked copies are deduplicated by turn ID; subagent work is excluded. Dates use each turn’s start in Pacific/Auckland. This export omitted 0 duplicate records, 0 records without a start time and 1 nonterminal records. An active day has at least one qualifying turn; zero means no qualifying record was retained.
This is a self-reported local snapshot, with coverage limited to retained records. It is not an official OpenAI export or an account-wide total, and it does not count completed features, commits, lines of code or hours worked. Only dates and aggregate counts are published; prompts, responses, workspace paths and credentials stay local.
The original migration account follows a personally maintained application from a Kubernetes lab to Cloud Run, Neon and static hosting, weighing infrastructure control against the work of operating it alone.
A follow-up on separate frontend and backend delivery paths in GitHub Actions, with Terraform for cloud configuration and remote state. The current case study adds implementation boundaries and release evidence.
Reflections from AI research and engineering practice on human judgment, inspectable outputs and the checks needed to support a decision, with verification as part of the working process.