About
I’m Cheng-Han Lin, a full-stack developer based in Taichung, Taiwan.
I graduated with a B.S. in Computer Science and Information Engineering from National Chin-Yi University of Technology in June 2026, and started the M.S. programme at the same university that September.
What I work on
My work keeps returning to one problem: how do you turn a large language model into something people can trust?
The model itself is the easy part to obtain. The hard part is everything around it — making sure it cannot invent an answer, keeping sensitive data inside a controlled environment, producing a useful forecast from only a few hundred rows per user, and deciding how the system should behave when it is wrong.
Concretely, I have built:
- Retrieval-augmented generation — vector retrieval over BGE-M3 embeddings in ChromaDB, where the model is required to cite the passage it drew from rather than answer freely.
- Self-hosted inference — open-weight models running on my own GPU service behind FastAPI, so highly sensitive text never leaves an environment I control.
- Machine-learning forecasting — feature engineering, model selection and cross-validation, traded off against interpretability, inference cost and overfitting risk.
- Backend and infrastructure — Django REST Framework, PostgreSQL, Cloud Run, Cloudflare, and mobile packaging.
- Billing and compliance — a complete subscription billing flow including statutory e-invoicing (ECPay, Taiwan’s e-invoice system).
What I care about
I care less about which technology was used than about why it was chosen.
Every decision costs something. Choosing Random Forest gives up whatever temporal structure a sequence model might have found. Tuning for recall means accepting more false alarms. Self-hosting a model means owning its operations. The difference between engineers shows up mostly in whether they can articulate those trade-offs.
The HeartBox case study on this site is written to that standard.
Contact
- Email: alan930604@gmail.com
- GitHub: alanlin0604
- LinkedIn: chenghanlin-tw