AI-powered textbook generation platform that transforms a topic into structured educational content with chapters, quizzes, flashcards, exams, images, and interactive learning experiences.
Live product: https://fessor.ai
This is a public system design and engineering showcase for my work on Fessor.ai, a deployed AI textbook generation platform. The production source code is private, so this repository focuses on architecture, product flows, technical contributions, and system design tradeoffs.
Some of the technical areas I contributed to include:
- Multi-model AI generation workflows
- Google OAuth integration
- Math rendering and KaTeX pipelines
- File attachments and OCR
- Realtime assistant experiences
- Exams, quizzes, and interactive learning
- Async task orchestration
- Security and platform reliability
→ See Engineering Contributions
Fessor orchestrates multiple AI models to generate complete textbooks and learning materials from a single prompt.
The platform combines:
- Structured chapter generation
- Interactive quizzes
- Flashcards
- Exams
- Page-level AI assistant
- Image generation
- Public textbook library
- Credit-based usage
- Multi-provider AI orchestration
Generate complete books from a topic prompt.
Includes:
- Chapters
- Sections
- Learning objectives
- Explanations
- Images
- Examples
Students can reinforce understanding through:
- Multiple choice quizzes
- Open-ended questions
- Flashcards
- Exams
Context-aware AI assistant capable of:
- Answering questions
- Explaining concepts
- Referencing page content
- Editing content
- Working with uploaded files
Users can:
- Browse community-created textbooks
- Search books
- Bookmark content
- Reuse materials
Supports:
- Rich text editing
- Attachments
- Learning objectives
- Section metadata
- Iterative refinement
Frontend:
- Next.js
- React
Backend:
- Django
- Django Channels
- Celery
Infrastructure:
- PostgreSQL
- Redis
- Docker
- Google Cloud Platform
See Architecture and System Design for detailed breakdowns.
- Next.js 15
- React 19
- TypeScript
- Django 4.2
- Django Channels
- Daphne
- Celery
- PostgreSQL
- Redis
- Docker
- Google Cloud Platform
- Cloud SQL
- Memorystore
- Cloud Storage
- OpenAI (GPT-4)
- Claude
- Gemini
- Grok (xAI)
- Architecture
- System Design
- AI Pipeline
- Async Workflows
- Realtime Architecture
- Contributions
- Future Improvements
Fessor is designed around three principles:
- Structured educational content
- Interactive learning experiences
- Scalable AI orchestration
This repository is intended as an architecture and technical showcase for the deployed product at Fessor.ai and does not contain the production source code.