I spend a lot of time around developers, architects and product teams figuring out what should be built, how it should run and whether it will still make sense after meeting real users, real traffic and real constraints.
Over the years, that has taken me through distributed databases, cloud infrastructure, APIs, Kubernetes, security and, more recently, AI infrastructure.
I am interested in the space between a good technical idea and a system that people can actually ship, operate and grow.
- What problem are we really solving?
- What happens when usage grows?
- What does the team have to operate?
- Where does complexity move?
- What drives the cost?
- What is the simplest architecture that still works?
Practical notes, small experiments, architecture patterns and useful open-source projects around:
- Cloud infrastructure and distributed systems
- APIs and developer platforms
- Kubernetes and infrastructure as code
- AI inference, gateways and model access
- Performance, reliability and infrastructure economics
- Akamai Cloud and the wider cloud-native ecosystem
I prefer examples that can be reproduced, diagrams that explain something and documentation that respects the reader's time.
Some things will be built from scratch. Some will be adaptations of good open-source work. Some will simply be lessons worth writing down.
I'm based in Ho Chi Minh City and work with teams across Vietnam, the broader Asia, and the US.
It is an interesting place to build technology: ambitious products, fast-growing usage, small teams, uneven infrastructure and very little patience for unnecessary complexity.
That combination creates good engineering questions.
- Akamai Developers — examples, workshops and reference projects
- Linode on GitHub — SDKs, CLI tools, Terraform, Kubernetes and open-source projects
- Akamai Cloud — compute, Kubernetes, storage, networking and GPUs