Hola, I’m Hieu, a Software Engineer

Analytics without a tracking plan becomes unusable noise. This article turns that observation into a small implementation model you can test, measure, and keep boring when the app grows. The problem in one sentence Naming, required properties, and versioning. The useful question is not whether the API or pattern looks elegant in isolation. It is where state lives, which boundary owns failure, and how a user recovers when the happy path disappears. A practical model Version the schema and review changes in PRs. Start with one explicit owner for the behavior. Keep widgets responsible for rendering and user intent; keep IO, persistence, permissions, and retries behind a small interface. That gives you a seam for a fake in tests and a place to record the facts that matter in production. For a Flutter app, the boundary usually looks like this: The widget emits an intent such as load, submit, refresh, or retry. A controller or use case validates the intent and calls the boundary. ...

AI features can burn gross margin overnight. This article turns that observation into a small implementation model you can test, measure, and keep boring when the app grows. The problem in one sentence Cost per request, free tiers, and routing cheap models first. The useful question is not whether the API or pattern looks elegant in isolation. It is where state lives, which boundary owns failure, and how a user recovers when the happy path disappears. A practical model Route by task difficulty; cache aggressively. Start with one explicit owner for the behavior. Keep widgets responsible for rendering and user intent; keep IO, persistence, permissions, and retries behind a small interface. That gives you a seam for a fake in tests and a place to record the facts that matter in production. For a Flutter app, the boundary usually looks like this: The widget emits an intent such as load, submit, refresh, or retry. A controller or use case validates the intent and calls the boundar...

Experiments need stable assignment and offline-safe defaults. This article turns that observation into a small implementation model you can test, measure, and keep boring when the app grows. The problem in one sentence Assignment stability, offline fallback, and metric hygiene. The useful question is not whether the API or pattern looks elegant in isolation. It is where state lives, which boundary owns failure, and how a user recovers when the happy path disappears. A practical model Define the primary metric before launch. Start with one explicit owner for the behavior. Keep widgets responsible for rendering and user intent; keep IO, persistence, permissions, and retries behind a small interface. That gives you a seam for a fake in tests and a place to record the facts that matter in production. For a Flutter app, the boundary usually looks like this: The widget emits an intent such as load, submit, refresh, or retry. A controller or use case validates the intent and calls t...