Landing Page
User Homepage
Record and Add Dream
Dream description
Dream analysis
Mood
Keywords

Inspiration

For many, dreams remain a hidden mystery, an enigma that holds a lot of fascination, yet remains largely unexplored. We, at DreamCatcher, believe in the power and potential of dreams. Understanding that dreams can provide deeper insights into our subconscious minds and that interpreting them can help in personal growth and self-awareness, we felt the need to create an intuitive platform where users can record, analyze, and learn from their dreams.

Goals

Create an AI-based platform that allows users to record and analyze their dreams.
Enable users to identify recurring themes and patterns in their dreams.
Provide psychoanalytic insights into the dreams.
Be open for future development and maintainable by the community.
Enable users to share and discuss their dreams with a community of dream enthusiasts.

Built With

NextJS and TailwindCSS for front-end
Advanced AI for transcribing voice recordings into text.
Sentiment Analysis for mood identification.
Keyword Extraction for identifying recurring themes and symbols.
Psychoanalysis algorithms for deeper dream insights.
Firebase for secure cloud-based storage.

Challenges

Ensuring seamless voice-to-text transcription.
Accurately interpreting moods, themes, and symbols from dream descriptions.
Building an intuitive and user-friendly interface.
Ensuring secure and private data storage for users' dream records.
Creating an engaging platform for dream discussion and feedback.

Accomplishments

We have developed DreamCatcher, an AI-driven platform that not only records and stores dreams but also provides deep, insightful analysis. It has an intuitive interface and leverages advanced technology to help users understand their subconscious mind better. The platform also offers community interaction, allowing users to share and discuss their dreams. Successfully implementing third party services we were using for the first time:

Used Hume.AI to extract emotions from audio
Used AnyScale to serve and scale multiple instances of Whisper

What We Learned

The development of DreamCatcher has taught us the importance of user-centric design, the potential of AI in interpreting human emotions, and the power of community collaboration in enhancing individual experiences. We learned how to navigate the challenges of integrating multiple advanced technologies into one platform.

What's Next

Integrate a machine learning model for trend analysis in dream patterns.
Introduce an option for dream visualization through images or illustrations.
Expand the community interaction section for enhanced user engagement.
Provide options for users to set reminders or alarms to record their dreams upon waking up.
Develop personalized insights and recommendations based on the analysis of individual dream patterns.