Adaptive study planning
LeetMentor
Help a learner decide what to practice next, based on what they have attempted, what they remember, and which topics still need coverage.
- 01INPUTPractice history and confidence
- 02SYSTEMMastery, coverage, and review planning
- 03OUTCOMEA focused next session
Repository grounded case study. No staged product demo.
WHY IT EXISTS
The product starts with the problem.
A long problem list does not create a useful study plan. Learners need a loop that remembers recent attempts, separates review from new material, and explains why a problem belongs in today’s session.
WHAT IT IS MEANT TO DO
Three jobs, one clear promise.
These are the product outcomes the repository is organized to deliver.
Replace random practice
The plan balances reinforcement problems with foundation problems instead of pulling from one undifferentiated list.
Keep progress explainable
Session history, confidence, mastery, and topic coverage remain visible so the next recommendation has a reason.
Train complexity intuition
A separate Big O drill tracks answers and mastery so algorithm analysis becomes part of the study loop.
PRODUCT FLOW
How the idea becomes useful.
A short path from the user’s starting point to a useful result.
- 01
Set the learner context
A public LeetCode username and local settings establish the problems and planning targets.
- 02
Record a session
Solved problems and self reported confidence become a durable history rather than a disposable checklist.
- 03
Update mastery
Topic signals and review timing shape reinforcement and foundation lanes.
- 04
Build the next session
The learner receives a smaller, purposeful queue and can continue into review or Big O practice.
ENGINEERING IDEA
The choices that define the product.
Architecture matters here because each constraint changes what a user can safely believe or do.
Local state by default
Study data lives in local JSON storage and browser stores, which keeps the project useful without a hosted account system.
AI as one part of the loop
Gemini supports grading and schedule generation, while the application still owns the session model, mastery state, and product flow.
Multiple feedback surfaces
Session work, history, settings, mastery, and Big O practice each answer a different part of the learning problem.
REPOSITORY PROOF
Evidence, not feature theatre.
Each point below comes from the current repository structure, code, or documented reference data.
Study surfaces
Home, session, history, settings, and Big O practice form the core product loop.
Local API routes
LeetCode data, mastery, storage, and Big O support the application.
Planning lanes
Reinforcement protects retention while foundation work expands coverage.
HONEST BOUNDARIES
What this product is not.
- LeetMentor is designed as a personal local study tool, not a shared learning platform.
- AI assisted features require a user supplied Gemini key.
- Recommendations organize practice, but they do not guarantee interview outcomes.