AI-Enhanced Roommate Compatibility Matching

Hivenue aims to enhance its platform by integrating an AI-driven matching system to improve roommate compatibility. The current system relies on basic user inputs and manual matching, which can lead to suboptimal pairings and user dissatisfaction. The goal of this project is to develop an AI algorithm that can analyze user profiles, preferences, and behavioral data to suggest highly compatible roommate matches. This will not only improve user satisfaction but also reduce conflicts and turnover rates. The project will involve researching existing AI matching algorithms, designing a custom solution tailored to Hivenue's needs, and implementing a prototype for testing and evaluation. Key points: - Enhance Hivenue's platform with AI-driven roommate matching. - Improve user satisfaction and reduce conflicts. - Research existing AI algorithms and design a custom solution. - Implement and test a prototype. Required technical kills : AI, machine learning, LLM Python NojeJS MongoDB

Automated Testing and DevOps Integration for Hivenue

Hivenue aims to enhance its platform's efficiency by automating its testing processes and integrating DevOps practices. Currently, the development pipeline involves manual testing, which is time-consuming and prone to human error. The goal of this project is to streamline the development pipeline by implementing automated testing frameworks and DevOps tools. This will not only reduce the time taken for testing but also improve the overall reliability and performance of the system. The project will involve setting up continuous integration and continuous deployment (CI/CD) pipelines, configuring automated testing tools, and ensuring seamless integration with the existing development environment. By the end of this project, Hivenue expects to see a significant improvement in system efficiency and a reduction in manual intervention during the testing phase.