Planix

Planix turns any syllabus into a personalized, day-wise study plan using AI. Paste your syllabus, set your time, and get daily cards with topics, subtasks, and progress tracking.

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Inspiration

As a student and developer, I often struggled with planning my studies effectively. Most of the time, I knew what to study but not how to break it down day-by-day in a realistic and structured way. Existing tools were either too generic or required too much manual effort.

I wanted to build something that could instantly convert a raw syllabus into a practical, daily study roadmap, something that thinks like a mentor, not just a checklist.

What it does

Planix is an AI-powered study planning platform that transforms any syllabus into a personalized, day-wise study plan.

Users simply paste their syllabus, select the number of days and hours per day, and Planix generates:

  • One structured study card per day
  • Topics to study
  • Actionable subtasks
  • Estimated time per topic
  • Optional revision suggestions

Each card has a simple progress status (To Do → In Progress → Done), making it easy to stay consistent without overwhelming the user.

How I built it

Planix is built as a modern full-stack web application using:

  • Next.js App Router with TypeScript for scalability
  • Tailwind CSS for a clean, minimal UI
  • Gemini AI to intelligently split the syllabus into logical daily chunks
  • PostgreSQL + Prisma ORM for persistent data storage
  • Clerk for secure authentication and user-specific study plans

The core challenge was designing prompts and data structures that ensure the AI output is practical, balanced, and time-aware rather than generic.

Challenges I ran into

  • Converting unstructured syllabus text into meaningful daily tasks
  • Ensuring realistic time estimation per day
  • Maintaining consistency between AI output and database schema

Accomplishments that we're proud of

  • Built an end-to-end AI-powered study planner that converts unstructured syllabus text into realistic, day-wise actionable plans.
  • Designed structured AI outputs with time estimates, subtasks, and revision logic instead of generic responses.
  • Implemented secure authentication and user-specific data persistence using Clerk, PostgreSQL, and Prisma.

What I learned

This project helped me deeply understand:

  • AI prompt engineering for structured outputs
  • Full-stack system design with authentication and persistence
  • UX decisions for productivity-focused applications
  • How to turn a real personal problem into a scalable product idea

What's next for Planix

  • Adaptive plans based on user progress
  • Smart revision reminders using spaced repetition
  • Collaborative study plans

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