In the emerging realm of decentralized commerce, the Beckn protocol emerges as a key enabler of innovation. This hackathon invites participants to develop groundbreaking applications utilizing the Beckn protocol, aiming to enhance collaboration and talent discovery within the Mulearn community.
Participants are tasked with creating a client application that harnesses the Beckn protocol to connect with various providers, showcasing talents across industries. This challenge focuses on designing a user-friendly, efficient client application for seamless talent discovery and interaction within the Mulearn ecosystem.
- A universal search bar for skill-based candidate search.
- A gig work exploration feature to find candidates available for various gigs.
- A platform for candidates to exhibit their portfolios and skills.
- Talent Discovery Opportunities: Explore how the Beckn protocol can facilitate talent and service discovery across industries within the Mulearn community.
- Build an Innovative Client Application: Develop a client application that effectively communicates with Beckn providers, showcasing decentralized talent discovery potential.
- Showcase Impact: Demonstrate the solution's significant impact in fostering collaboration, connecting professionals, students, or individuals with specific skills.
- Innovation and User Experience: Creativity in approaching talent discovery using the Beckn protocol.
- Impact on Talent Showcase: The potential impact of the client application in showcasing talents within the Mulearn community.
- Effective Use of Beckn Protocol: Efficiency in utilizing the Beckn protocol for provider interaction and talent discovery.
- Technical Feasibility: Consideration of scalability and usability within the Mulearn ecosystem.
- The clarity and persuasiveness of the project pitch, focusing on how well the solution meets the objectives of talent discovery.
Participants are encouraged to think innovatively, leveraging the Beckn protocol to create solutions that enhance collaboration and talent discovery across various domains within the Mulearn community.