DinoCode is a placement-preparation platform designed for Indian engineering students preparing for coding assessments, campus placements, and product-company interviews.

Instead of functioning as another static DSA or LeetCode problem list, DinoCode helps students build a consistent preparation habit through personalized daily tasks, timed practice, revision schedules, and clear readiness signals. Students choose their target pathway and deadline, then receive a structured plan that identifies what to practise next and what concepts need revision.

Key product capabilities:

1. Personalized daily DSA plans based on target, deadline, and available preparation time

2. Curated learning, revision, and timed-practice tasks

3. Spaced-repetition system to help students retain solved patterns

4. LeetCode progress import and historical tracking

5. Offline-first workflow with local caching and secure sync when connectivity returns

6. Explainable readiness indicators based on pattern coverage, consistency, revision strength, diagnostic results, and timed performance

7. Placement-focused preparation sprints, including short OA rescue plans and longer product-company DSA tracks

8. Secure Supabase/PostgreSQL backend with role-based access controls, typed APIs, idempotent offline syncing, and protected payment-entitlement architecture

My contribution:

I led the product strategy, system design, UX direction, database architecture, and implementation planning for DinoCode. I designed the shift from a company-wise DSA tracker into a student-first Placement Readiness OS focused on retention, structured preparation, and measurable progress.

The technical design includes React, Supabase PostgreSQL, Row Level Security, controlled database RPCs, IndexedDB offline queues, TanStack Query, and a hybrid local/cloud data model.

Product vision:

DinoCode aims to make placement preparation less overwhelming by answering one practical question every day: