Resumind
On-Chain AI Resume & Talent Matching on Lisk
About
The problem it solves ## For Job Seekers * Many applicants are rejected before human review due to poor ATS optimization. * Users often do not know why their resumes fail or how to improve them. * Resume advice online is generic and not tailored to specific job descriptions. ## For Recruiters * Sorting through thousands of resumes is time-consuming. * Traditional hiring platforms rely on centralized databases and opaque scoring systems. * There is no reliable, transparent way to quickly filter candidates based on job-specific resume compatibility.
How it's made
Challenges we ran into ## Challenges Faced During Development During the development of this project, I encountered several key challenges related to the Lisk ecosystem and full-stack integration: ### 1. Network Configuration & Documentation Alignment - Some **network configuration values were outdated**, requiring additional effort to correctly configure the environment for deployment and testing. - The **smart contract `.config` file** and related setup instructions in the documentation needed updates to reflect current network parameters. - This caused **initial friction**, especially when switching between development and test networks. ### 2. Limited End-to-End Integration Examples - While the core blockchain documentation explains contract interaction, there were **few examples connecting smart contracts to real-world applications**. - Integrating smart contracts seamlessly with **frontend applications** required extra research and experimentation. - This challenge highlighted the need for **more full-stack guidance** for developers building decentralized applications. ### Summary These challenges underscore the importance of: - Up-to-date network configurations - Clear, end-to-end examples - Seamless integration guidance for developers Overcoming these hurdles allowed the project to achieve **robust deployment and reliable frontend-backend interactions** within the Lisk ecosystem.