About the Project

Inspiration

The world of finance can be overwhelming, especially for beginners. Many people struggle with understanding the basics of financial literacy, let alone making informed investment decisions. We wanted to create a roadmap-like platform that simplifies this journey—guiding users step by step with AI-powered insights.

What We Built

Our platform is designed to be an AI-driven financial education and investment guide, breaking down complex concepts into an interactive, personalized learning experience.

Key Features:

• AI-powered classification to filter relevant financial news.
  
• Guided investment strategies tailored to different user goals.
  
• Roadmap-style progression, making financial literacy structured and intuitive.
  
• Dynamic lesson generation using Claude AI to create comprehensive educational content.
• User authentication system to maintain personalized learning paths.
• RESTful API architecture enabling seamless frontend-backend communication.
• Incremental learning unlock system to gradually introduce complex concepts.

Technical Implementation

We built our solution using a modern tech stack that prioritizes scalability and responsiveness:

• Backend: FastAPI framework providing high-performance API endpoints
• Database: SQLAlchemy ORM with PostgreSQL for robust data management
• AI Integration: Anthropic's Claude API for generating customized educational content
• Authentication: JWT-based token system for secure user sessions
• Content Structure: Markdown-based lesson format for rich, well-structured educational material

The system generates personalized financial education roadmaps based on user interests and skill levels. Each roadmap contains a series of lessons that unlock progressively as users advance through their learning journey. The content is dynamically generated using Claude AI to ensure each lesson is comprehensive, accurate, and tailored to the specific topic.

What We Learned

Throughout the development process, we gained deeper insights into:

• Natural Language Processing (NLP) for financial news filtering.
  
• AI integration for personalized learning and investment guidance.
  
• Backend development for handling real-time data efficiently.
• Hosting our backend as an API globally with ngrok!: This was very cool, and enabled us to use our endpoints in Retool, globally.
• User experience (UX) design to make finance approachable for all skill levels.
  
• Prompt engineering to extract high-quality educational content from AI models.
• API design patterns for building scalable, modular applications.
• Error handling strategies to ensure system reliability even when AI services fail.
• Database schema optimization for efficient query performance.

Challenges We Faced

• Actually getting everything to work together, or trying to: It was most difficult in the end to make a front-end that conveyed the work we put into our dozens of files in the backend. We tried flask, react, and eventually switched to retool in the last stretch because it was the only realistic way we could get a decent front-end up and running.
• Data filtering: Accurately classifying financial news while avoiding irrelevant content.
  
• AI integration: Ensuring the AI model provides valuable, accurate investment insights.
  
• Balancing simplicity & depth: Making financial education accessible without oversimplifying key concepts.
  
• Content consistency: Maintaining a uniform quality and structure in AI-generated lessons.
• Error resilience: Building fallback mechanisms for when AI generation fails.
• Authentication security: Implementing robust user authentication while maintaining simplicity.
• Performance optimization: Ensuring AI-generated content doesn't impact application responsiveness.
• Database design: Creating relationships between users, roadmaps, and lessons while maintaining flexibility.

Technical Achievements

We're particularly proud of several technical accomplishments:

1. Structured AI Content Generation: Our system generates consistently formatted educational content using a well-defined schema and prompt engineering.

2. Progressive Learning System: We implemented a mechanism that tracks user progress and unlocks new lessons automatically, creating a gamified learning experience.

3. Resilient API Architecture: Our endpoints handle various edge cases, including AI service failures, database constraints, and authentication issues.

4. Modular Design: We built the system with clear separation of concerns, making it easy to extend and maintain as we add new features.

What's Next?

We plan to expand our AI models, improve real-time investment tracking, and refine the user experience to make financial literacy effortless and engaging for everyone. Specific enhancements include:

• Enhanced AI models: Fine-tuning our prompts to generate even more tailored financial education content.
• Interactive assessments: Adding quizzes and exercises to test and reinforce learning.
• Social learning features: Implementing community discussion and peer learning opportunities.
• Mobile application: Developing a dedicated mobile app for learning on the go.
• Progress analytics: Creating dashboards to help users visualize their learning journey.
• Custom roadmap generation: Allowing users to generate entirely customized learning paths based on their specific goals.
• Integration with financial APIs: Connecting with real market data to provide practical, real-time examples.

This hackathon has been an incredible learning experience, and we're excited to continue developing this platform to make financial literacy accessible to everyone, regardless of their background or experience level.

Built With

• anthropic-claude-api
• javascript
• newsapi
• openai-api
• postgresql
• python
• railway
• retool
• sql