- Inspiration
- What it does
- How we built it
- Challenges we ran into
- What's next for Failed-in
- Contributors
- Images
On social media, most of the things that come up are success stories. We've seen a lot of our friends complain that there are platforms where people keep bragging about what they've been achieving in life, but not a single one showing their failures.
We realized that there's a need for a platform where people can share their failure episodes for open and free discussion.
So we have now decided to take matters in our own hands and are creating Failed-In to break the taboo around failures! On Failed-in, you realize - "You're NOT alone!"
- It is a no-judgment platform to learn to celebrate failure tales.
- Enabled User to add failure episodes (anonymously/non-anonymously), allowing others to react and comment.
- Each episode on the platform has #tags associated with it, which helps filter out the episodes easily. A user's recommendation is based on the #tags with which they usually interact
- Implemented sentiment analysis to predict the sentiment score of a user from the episodes and comments posted.
- We have a motivational bot to lighten the user's mood.
- Allowed the users to report the episodes and comments for
- NSFW images (integrated ML check to detect nudity)
- Abusive language (integrated ML check to classify texts)
- Spam (Checking the previous activity and finding similarities)
- Flaunting success (Manual checks)
- We used Node for building REST API and MongoDb as database.
- For the client side we used flutter.
- Also we used tensorflow library and its built in models for NSFW, abusive text checks and sentiment analysis.
- It was the first time we tried using Flutter-beta instead of React with MongoDB and node. It took a little longer than usual to integrate the server-side with the client-side.
- Finding the versions of tensorflow and other libraries which could integrate with the remaining code.
- Improve the model of sentiment analysis to get more accurate results so we can understand the users and recommend them famous failure to success stories using web scraping.
- Create separate discussion rooms for each #tag, facilitating users to communicate and discuss their failures.
- Also provide the option to follow/unfollow a user.
Built At HACK HARVARD