Pravah-X is a comprehensive, full-stack web platform designed specifically for beginner and intermediate competitive programmers. It eliminates the friction of environment setup by unifying a personalized IDE, an AI debugging assistant, daily problem tracking, and live Codeforces analytics into a single, intuitive interface. By streamlining the workflow, Pravah-X allows developers to focus entirely on algorithmic problem-solving rather than boilerplate configuration.
Real-time synchronization with the Codeforces API to display user rating history, an activity heatmap, and recent submission verdicts.
An integrated coding environment pre-loaded with standard C++ templates, coupled with an AI chatbot to help debug code and explain complex algorithmic concepts.
Features a curated Problem of the Day, a topic-wise learning roadmap, and a 30-day visual streak tracker to build consistency.
A real-time feed of upcoming Codeforces rounds complete with countdowns so users never miss a competition.
Robust credential and OAuth support handled via NextAuth.js, with protected routes managed by Next.js middleware.
Built on the Next.js App Router using React 19 and Tailwind CSS, featuring a responsive UI with smooth framer-motion transitions and dynamic data visualization via Recharts.
Utilizes Next.js API routes to handle custom backend logic, user registration, and secure communication with external APIs.
A PostgreSQL database managed by Prisma ORM ensures type-safe querying and reliable storage for user sessions, profiles, and learning path progress.
Directly interfaces with the official Codeforces API to fetch and cache live user statistics, contest schedules, and submission data.
Integrate a secure sandboxed backend (e.g., using Docker or a third-party compilation API) to allow users to compile and run their code directly within the Pravah-X IDE.
Expand the analytics and problem-tracking capabilities to include other major competitive programming platforms like LeetCode and AtCoder.
Upgrade the AI assistant to perform automated full-code analysis, providing detailed feedback on time and space complexity and suggesting optimal algorithmic approaches.