Learning
Studying in public
A public study log, honest about being one: what I am actively learning, what I have already tested in real projects, and what is still planned. The through-line is deliberate practice — fundamentals before frameworks, evaluation before claims, and critical review of anything my AI tools produce before it ships. The reading runs on a written method I now lead a professional circle on: brief a book before page one, recall each chapter closed-book, ship an explanation or a one-week experiment in real work, then review at 48 hours, 7 days, and 30 days.
AI & Machine Learning
Building from both ends: classic ML fundamentals in Python and C++ for depth, and fine-tuning real speech models for Khmer for practice — low-resource constraints punish cargo-cult ML quickly.
- Classic machine-learning fundamentals — supervised models, evaluation discipline, and the Python and C++ underneath them
- Speech-model fine-tuning and feature pipelines (Whisper-family models)
- Evaluation design for Khmer text, where character error rate beats word error rate
- Error analysis by speaker, device, and noise condition
- Writing model cards that state dataset provenance and failure modes
Software & Product Systems
Learning to design systems before writing them, ship small typed and tested slices, and lead AI coding agents the way a reviewer leads a team — critically, with explicit approval gates.
- AI-native development workflows — leading coding agents, reviewing their output critically, and keeping human approval gates explicit
- System design — decomposing a product into components, data flows, and failure modes before writing code
- Deterministic pipelines with unit tests and CI
- Typed content architectures and schema validation
- Static-site engineering, deployment workflows, and rollback thinking
Business & Strategy
Learning to test commercial assumptions in the field before writing code — and to tell the story afterward, through digital marketing and bilingual technical content.
- International business coursework
- Digital marketing and technical content — including short-form video editing in CapCut for English/Khmer developer content
- Unit economics of agricultural coordination (spoilage, payment timing, fulfillment)
- Buyer-first validation instead of marketplace-first assumptions
Governance & Commercial Rules
Learning how contracts, terms, and policies decide what AI systems may do — using research agents to survey the primary literature, as a builder who reads the rules, not as a lawyer.
- Using AI research agents to survey technology-law papers and pull out what actually applies to my products
- Contracts and terms of service for software and AI products
- Data licensing, consent, and privacy for speech datasets
- Risk assessment and human-approval design patterns