It began as a personal tool. I wanted one place to see what I had studied, what I was getting wrong, and whether I was actually improving rather than just doing more work.
Over time the idea became broader: combine score tracking, targeted practice, and explanation-based learning so revision feels less fuzzy and more measurable.
I built Gradelyy because I wanted one place to keep track of my revision for the GMAT exam. I did not want revision scattered across notes, tabs, question banks, and vague feelings about whether I was improving.
The Feynman technique is powerful because explaining a topic exposes what you do not really understand. LLMs make that possible when you are studying alone, without needing a friend sitting there to question you back.
Score tracking, weak-area detection, and seeing trends over time are simply easier in software. You can look at your revision and know where you are strong, where you are slipping, and what needs another pass.
A model on its own is just a prediction engine. The leverage comes from the loop around it: your attempts, your mistakes, your explanations, your scores, and the extra context that keeps sharpening what the system shows you next.
Modern enterprise models are good enough to help with content generation and guided practice when the material is not at the very highest specialist level. The value is not in pretending they are magic. The value is in placing them inside a structured system.
LLMs work best when they get more and more context. They are prediction machines, so the more work you feed through an agentic layer, the better they get at spotting patterns in mistakes, missed topics, and weak areas.
Gradelyy began as a personal-use tool rather than a polished grand plan. The goal was simple: make revision easier to track, harder to fake, and more useful when studying alone.
Make revision easier to track. Use explanation as a learning tool. Keep feeding the system better context so it gets better at finding the next thing to work on.