Inspiration
The inspiration for Discern AI came from the growing need for reliable information in today's digital age, with misinformation spreading rapidly across social media and other online platforms, we wanted to create a tool that empowers users to verify facts easily and efficiently. The idea was to harness the power of cutting-edge language models and API integrations to provide an accessible solution for fact-checking.
What it does
is a powerful fact-checking tool designed to help users distinguish between truth and misinformation. By simply inputting a text or a URL—whether it's a news article, a social media post, or any online claim—our platform delivers quick and reliable verification. This empowers users with accurate information
How we built it
The project was built using a combination of technologies:
Frontend: We used HTML, Tailwind CSS, and JavaScript to create a responsive and intuitive user interface.
Backend: The server was built with a Python Flask app that ran the GROQ console through an API to utilize the LLM model for fact-checking.
APIs: We integrated several APIs that provide data verification services, enabling users to input text or URLs for validation.
LLM Integration: Advanced language models were utilized to analyze and assess the credibility of the inputted information.
Challenges we ran into
One of the major challenges we encountered was hosting both the backend and frontend on two different platforms accurately. This involved:
CORS Issues: We faced cross-origin resource sharing (CORS) issues while trying to connect the frontend hosted on one platform to the backend on another.
Deployment Configurations: Each platform had its deployment requirements, which made it challenging to ensure that both parts of the application communicated effectively.
Accomplishments that we're proud of
We are particularly proud that this is our first hackathon project. Despite being newcomers, we successfully collaborated and delivered a functional product that addresses a pressing social issue/educational issue. The experience taught us valuable teamwork skills and allowed us to apply theoretical knowledge in a practical setting.
What we learned
Throughout the development of Discern, I gained valuable insights into:
API Integration: we learned how to effectively integrate APIs to fetch and verify data from multiple sources.
Frontend and Backend Development: Working on both the frontend and backend helped us understand the intricacies of full-stack development, including user experience design and server-side logic.
Handling Misinformation: we gained a deeper understanding of the challenges associated with identifying and categorizing misinformation, as well as the importance of presenting users with accurate information.
What's next for Discern AI - Misinformation bot
Looking ahead, we plan to enhance Discern AI by implementing several key features:
Login/Signup Feature: We will introduce a user authentication system to allow users to create accounts, manage their profiles, and track their activity.
User Education: We will develop educational resources that provide tips and strategies for identifying misinformation, helping users become more discerning consumers of information.
Community Engagement: We aim to create community features such as forums or discussion boards where users can share insights, report misinformation, and collaborate on fact-checking.
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