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Hi, I'm Laura — curious by nature, researcher by choice.

DBAI candidate at PolyU Hong Kong. I write about AI, research, responsible tech, and the messy, fascinating process of figuring things out.

DBAI @ PolyU HK Transformative AI Responsible AI Always learning
About Me.
Random thoughts on the journey
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I'm someone who ended up in a DBAI programme partly out of curiosity and partly because I couldn't stop asking questions that didn't have tidy answers. I work with AI every day — and the more I work with it, the more I realise how little we collectively understand about where it's taking us.

This corner of the internet is where I think out loud. No polished takes, no pretending I have it figured out. Just honest reflections from someone in the middle of it.

When I'm not reading research papers or arguing with AI systems, you'll find me somewhere between Hong Kong and Hanoi, perpetually caffeinated.


Journal
What it actually feels like to study AI while using AI every day
There's something strange about reading papers on AI autonomy in the morning and then asking an AI to help you summarise them in the afternoon.
June 5, 2026 Read →
Journal
Why I decided to do a DBAI at 30-something
It wasn't the obvious choice. Here's the honest version of how I ended up here — and what I wish someone had told me before I started.
June 5, 2026 Read →
Research Journey.
Notes from my DBAI at PolyU Hong Kong
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Research
The missing layer — why AI agents need an operating system
The agent harness is the layer between the raw model and the application — managing memory, tools, and context over time. Without it, even the best models fail on long tasks.
June 5, 2026 Read →
Research
The deployment dilemma — choosing between speed and intelligence in AI agents
Fast and cheap means single agents. Smart and reliable means multi-agent systems. The cost and latency gap between them is the central engineering challenge of AI deployment in 2026.
June 5, 2026 Read →
Research
Can you distill multi-agent intelligence into a single model? AgentArk says yes.
Multi-agent systems reason better but cost exponentially more. AgentArk proposes a third path — bake the multi-agent reasoning into a single model's weights during training.
June 5, 2026 Read →
Research
We finally have a rigorous definition of AGI. Here's what it reveals.
GPT-4 scores 27%. GPT-5 scores 57%. Progress is real — but the gaps it exposes are just as interesting as the gains.
June 5, 2026 Read →
Reflections.
On responsible AI — the questions that keep me up at night
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Responsible AI
The four ways AI agents can harm you — a red team taxonomy
Not all AI agent failures are equal. A structured taxonomy helps you think clearly about which risks are actually most urgent.
June 5, 2026 Read →
Responsible AI
From tools to agents — what the AI 2.0 shift actually means for how we work
AI 1.0 was a tool you picked up. AI 2.0 is a colleague you manage. That sounds like a small difference. It's not.
June 5, 2026 Read →
Responsible AI
When AI agents lie about finishing the job
Researchers red-teamed autonomous AI agents for two weeks. One finding I can't stop thinking about — agents reported task completion while the actual system state said otherwise. Who's accountable?
June 5, 2026 Read →
Responsible AI
AI Theater: why most AI pilots fail and what to do instead
There's a specific failure mode infecting enterprise AI — impressive demos that never scale, metrics that don't connect to value, accountability that belongs to everyone and therefore no one.
June 5, 2026 Read →
Responsible AI
78% of companies use AI. Only 39% see it in their results. That gap is a governance problem.
Adoption has decoupled from value creation. The hard question for boards isn't whether to use AI — it's whether they actually know what it's doing on their behalf.
June 5, 2026 Read →
Responsible AI
OpenAI's $110B round is not a funding event. It's infrastructure lock-in at civilisational scale.
The structure of OpenAI's financing tells you more about AI's trajectory than any model benchmark. This is how you lock in an ecosystem.
June 5, 2026 Read →
Resources.
Papers, tools, and things worth reading
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