Hi, I’m Zhimin.

I build things to understand how they work in the real world.

I have spent most of my career in public service, working across strategy, operations, community engagement, organisational change and increasingly, technology.

This site is where I keep the things I’ve built, the work that shaped how I think, and what I’m exploring now.

Singapore · Programme management · Digital products · Change · AI & automation
Problems worth solving
People doing the work
Systems & constraints
Technology as an enabler
BUILDto learn
Things I’ve built

I build to learn.

Some projects begin with a problem I run into. Others begin with a question I want to test. They range from decision-support tools and searchable libraries to learning games and AI-enabled experiences. Taking an idea far enough to become usable forces me to think more clearly about the user, the trade-offs and what the technology can — and cannot — do.

Decision support

P1 Compass

Exploring how Primary 1 registration information can be organised around the decisions parents actually need to make.

User journeyPublic informationSimplification
Open P1 Compass ↗
The project started from a familiar frustration: the information existed, but the decision was still hard. Building it made me think much more carefully about information architecture, cognitive load and how public information can be structured around a user’s question rather than an organisation’s structure.
Search & information

PaperKaki

A searchable collection of Singapore primary-school examination papers, built around discoverability and filtering.

SearchFilteringScale thinking
Open PaperKaki ↗
What looked like a simple search problem quickly became a lesson in data structure, storage, authentication, bulk actions and the difference between a prototype that works and a service that can be sustained.
AI product thinking

Coach

Exploring where AI can genuinely help turn assessment information into more targeted practice, feedback and useful next steps.

AI workflowPersonalisationHuman judgement
Open Coach ↗
The interesting part was not simply generating questions. It was learning where structured data, model reliability, user trust and human judgement matter if an AI-enabled product is meant to be genuinely useful.
Immersive learning prototype

We-First Journey

Reimagining existing brand assets and training content as a more game-like learning journey instead of another page of material to click through.

GamificationLearning experienceExisting content
Open We-First Journey ↗
I wanted to test whether the same learning content could feel meaningfully different when the experience changed — using exploration, progression and game-like interactions to draw the learner through it, without having to rebuild the underlying curriculum from scratch.
Interactive e-learning

MAKE Something

A short learning experience designed to make vibe coding approachable to people who do not see themselves as coders.

Vibe codingMicrolearningLearning-by-doing
Open MAKE Something ↗
The central idea is simple: learners do not need to begin with code syntax. They can begin with the outcome they want, make a few human decisions and learn how to guide an AI agent from idea to working preview. The prototype let me explore how a technical concept can be taught through interaction rather than explanation alone.
Digital learning prototype

Systems Lens

An interactive pre-read that helps leaders grasp core Systems Leadership concepts before class, creating more time for application, discussion and judgement during the programme.

Leadership learningDigital pre-workHuman-in-the-loop AI
Open Systems Lens ↗
I wanted to test whether foundational leadership content could be moved out of the classroom without turning the pre-read into another document to skim. The prototype explores how digital interaction, reflection and AI-supported prompts can prepare learners for a richer face-to-face discussion.
Capability building

Chatbot Architect

A Pair Chat design assistant built for a workshop I ran with public service leaders and managers, guiding them through the six questions behind a good custom chatbot — purpose, audience, experience, knowledge, guardrails and testing — before they write a single prompt.

FacilitationGenAI enablementDesign frameworks

Chatbot Architect only opens on Whole-of-Government (WOG) laptops, since it runs on Pair Chat. The guide above is open to everyone.

Most people think chatbot creation starts with writing a good prompt. It does not — it starts with knowing the work process you are trying to design. Running the workshop showed me how much friction beginners hit at the blank page, so I built Chatbot Architect to walk them through that thinking before they touch a prompt, and wrote the guide so the same design flow still works even without the tool.
How I got here

The work changed. So did the way I looked at problems.

There was no single point where I decided to “move into technology”. It happened gradually. Each stage of my career exposed me to a different part of the system and pulled me closer to the next question.

Understand the system.Planning, operations, service and organisational alignment.
Get closer to the ground.Context changes what a good answer looks like.
Learn what adoption really takes.Technology has to fit the work around it.
Move closer to the product.Requirements, procurement, beta, launch, support and adoption.
Build closer to the problem.Automation, AI and rapid prototyping shorten the loop.
01 · Foundation

Learning how organisations work.

My early roles in international operations, strategic planning, service excellence and organisation development gave me a lasting bias towards understanding the system before designing the intervention.

Strategic planningOperationsOrganisation development
02 · Ground

Seeing the difference between a plan and the work.

Working closely with constituency teams, community leaders and staff changed my perspective. A strategy can make sense at headquarters and still be difficult to apply in a particular operating context.

Ground sensingStakeholder relationshipsResident engagement
03 · Change

Learning what adoption really takes.

PRISM — a resident-data system rolled out across PA’s constituency network — reinforced that the challenge is not simply teaching people to use a system. It is helping the technology fit alongside new processes and ways of working.

87 constituencies1,700+ grassroots leaders500 staff
04 · Product

Moving closer to the product lifecycle.

A gamified digital learning application brought me through requirements, public-sector procurement, vendor discussions, development, beta testing, launch and post-launch adoption.

276 users took part in a three-month beta, generating more than 350 pieces of feedback.

RequirementsRFQBetaAdoption
05 · Now

Building closer to the problem.

More recently, I have used government digital platforms to automate work, built AI-enabled assistants for programmes, experimented with enterprise AI, and started building my own products through AI-assisted development.

One attendance and RSVP workflow reduced manual administrative work by 40%.

FormSGPlumberPairAI-assisted development
How I think

A few ideas I keep coming back to.

These are working principles rather than grand theories. They have simply proved useful across different roles and problems.

01

Start with the problem, not the tool.

Technology matters when it changes the quality, speed or economics of solving something real.

02

Go-live is where reality begins.

Usage, support, behaviour, maintenance and improvement are part of the product.

03

Build enough to understand.

I do not need to be the engineer in the room, but building makes me better at asking useful questions.

04

Keep the core strong. Leave room at the edges.

Shared platforms matter. So does giving teams safe ways to solve legitimate local needs.

Now

What I’m exploring at the moment.

A large part of my curiosity now sits around how AI, reusable platforms and new interaction patterns change what small teams can build — not only tools, but also better ways to learn, explore and make decisions.

Question 01

How does agentic AI change what a small team can build?

I am interested in where agents genuinely remove friction, and where they simply add another layer of complexity.

Question 02

How should shared platforms support the last mile?

The challenge is giving teams flexibility without creating a new generation of disconnected point solutions.

Question 03

Where should human judgement stay firmly in the loop?

As AI becomes embedded in everyday work, the interesting design question is not only what can be automated, but what should not be.

About

A little about me.

I’m based in Singapore and have spent my career in public service since 2009. The work has moved through planning, service excellence, community-facing roles, capability development, change programmes and digital products.

“I tend to enjoy problems that sit between functions and do not have a neat owner.”

Different people can all be reasonable and still see the same problem differently. My usual instinct is to get close to the people doing the work, make the problem concrete, and find something we can test.

I’m still learning. A lot of what you see on this site exists because building something is often the fastest way for me to discover what I do not yet understand.

I like getting close to the actual work.

That is why ground engagement and direct user feedback matter to me.

I’m interested in technology, but not for its own sake.

The question is whether it makes the work better, simpler or more sustainable.

I’m comfortable saying “I don’t know yet”.

Pilots and prototypes are often more useful than pretending the answer is obvious at the start.

I care about what happens after launch.

Adoption and support are part of the solution, not someone else’s problem.

Contact

If something here sparked a question, I’d be happy to continue the conversation.

I have kept this site intentionally selective. There is much more detail behind the work here — what worked, what did not, what I learnt and what I would do differently today.