Senior Backend Developer specializing in Java, Spring Boot, and microservices architecture. I care about clean architecture and solid coding practices — this site showcases my projects, technical decisions, and what I’m currently exploring.
A flashcard tests recall, not understanding. Flip the card, read the answer, and the question “did I actually get it?” is answered by you, and you are the least reliable judge in the room. Overconfidence is the default: I kept believing I knew a word while being unable to use it in a sentence, and the app had no way to tell the difference.
I use LexiStack to learn vocabulary and technical concepts for myself, and that gap, between “I recognize this” and “I can use this”, is exactly what a tutor closes. So I built one in: for every card in a study session, the app now asks me to do something with the term, an LLM checks whether my answer actually shows understanding, and if it doesn’t, it explains the gap. The AI never grades the card on its own. It suggests, and I decide.
LexiStack already used an LLM to generate content (card suggestions, deck generation); this was the jump to AI as an interaction layer during the study session. And it raised the question that shaped every decision below: how do you add AI to a feature whose core loop, the review plan that decides when each card comes back, must never break?
Here we are going to understand how to manage errors in Spring Boot Rest applications.
This principle about Interface segregation principle (ISP) says that any client should not be required to depend on methods they do not use.