Computer Science @ UNT | Web Developer | Expert in React, JavaScript, and TypeScript

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Raymond Oyondi  • Peerlist

ResumeAI is an innovative full-stack web application designed to revolutionize the way job seekers create resumes. By leveraging the power of AI, this platform transforms raw career data into professional, ATS-friendly resumes in minutes. Key Features include AI-powered content generation that crafts compelling bullet points and professional summaries, ensuring your resume passes through Applicant Tracking Systems with built-in keyword analysis. The platform offers a real-time live preview, allowing users to see changes instantly with a side-by-side editing interface. Secure Authentication is provided through modern auth providers, ensuring user data protection. Users can choose from a variety of customizable templates designed to be readable by both humans and machines. Additionally, ResumeAI supports high-quality PDF export, making your resume ready for job applications. Tech Stack includes React, Next.js, Tailwind CSS for the frontend, and Node.js with Express for the backend. AI integration is achieved through Google Gemini API and OpenAI API, with PostgreSQL and MongoDB as database options. Authentication is managed by Clerk and NextAuth.

ResumeAI - AI Resume Builder

PromptStore is a cutting-edge AI Prompt Marketplace that empowers creators to monetize their prompt engineering skills while providing users with a curated library of high-quality prompts for models like GPT-4, Midjourney, and Claude. Built with a serverless-first architecture, PromptStore ensures scalability, speed, and cost-efficiency. The platform is designed using a modern tech stack including Next.js for the frontend, Node.js and TypeScript for serverless backend functions, and Prisma ORM with a scalable PostgreSQL database. Authentication is managed through NextAuth.js or Clerk, and payments are seamlessly handled with Stripe Integration. Deployment is managed via Vercel for high-availability hosting. Key features include browsing and filtering prompts by category or AI model, a user dashboard for sellers to track sales and manage listings, secure checkout with instant access to purchased prompts, and a responsive design optimized for all devices. The serverless approach allows the application to scale to zero when not in use, significantly reducing operational costs while maintaining the ability to handle traffic spikes.

PromptStore - AI Prompt Marketplace

CampusSwap is a comprehensive full-stack marketplace platform tailored for students, facilitating the buying, selling, and trading of items and services within campus communities. Built on the robust MERN stack (MongoDB, Express, React, Node.js) and enhanced with PostgreSQL for handling relational data, this application ensures a seamless and secure user experience. Key features include:<ul><li>Secure Authentication: Utilizes JWT for user login and registration, with student email verification for added security.</li><li>Hybrid Database Architecture: Combines the flexibility of MongoDB for real-time data with the reliability of PostgreSQL for structured transactions.</li><li>Real-Time Messaging: Enables instant communication between buyers and sellers through Socket.io.</li><li>Robust Search &amp; Filters: Allows users to filter listings by category, price range, or item condition.</li><li>Dynamic Listings: Users can easily upload, edit, and manage their items, complete with image support.</li></ul>The application is designed with a modular architecture to ensure scalability, featuring a responsive React SPA client, a Node/Express API server, and a data layer that supports both ACID compliance and horizontal scaling. Open source and community-driven, CampusSwap invites contributions to enhance its features and usability.

CampusSwap - Student Marketplace

On-Orbit Collision Predictor is a comprehensive MATLAB-based tool designed for satellite conjunction analysis and space debris collision risk assessment. As Earth's orbit becomes increasingly congested, the risk of catastrophic collisions grows exponentially. This project provides a robust framework to predict future positions of space objects using TLE (Two-Line Element) data, detect "Close Approach" events within a defined spatial volume, and quantify the probability of collision using established statistical models.<h3>Key Features</h3><ul><li>SGP4/SDP4 Propagation: High-fidelity propagation of TLE data sets to account for atmospheric drag, Earth oblateness, and solar/lunar perturbations.</li><li>TCA Determination: Accurate calculation of the Time of Closest Approach using iterative minimum-distance algorithms.</li><li>Probability Analysis: Implementation of Foster’s and Chan’s algorithms for collision probability estimation.</li><li>Covariance Transformation: Mapping of positional uncertainty ellipsoids into the encounter frame.</li><li>Visualizations: 3D orbital trajectory plotting and relative distance-over-time graphs.</li></ul>This tool is essential for ensuring satellite safety and managing space debris, providing users with the ability to propagate orbits, identify conjunctions, and assess collision risks effectively.

On-Orbit Collision Predictor

HoopsForecast is an advanced machine learning project designed to forecast player statistics for upcoming NBA seasons. This project leverages a comprehensive data science pipeline, integrating automated data scraping, feature engineering, and sophisticated regression modeling to provide accurate predictions. The core functionality is powered by an interactive web dashboard application, allowing users to visualize performance predictions and explore various 'what-if' scenarios.<h3>Key Features</h3><ul><li>Automated Data Pipeline: Efficiently scrapes and cleans player-per-game stats, advanced metrics, and team standings.</li><li>Feature Engineering: Calculates rolling averages, career trajectories, and age-based performance curves to enhance prediction accuracy.</li><li>Multi-Model Comparison: Utilizes Linear Regression, Random Forest, and Gradient Boosting (XGBoost) models to evaluate and improve prediction outcomes.</li><li>Web Dashboard: Offers an interactive interface for users to visualize individual player projections and analyze potential outcomes.</li></ul>The project is built using a robust tech stack including Python, Pandas, NumPy, and Scikit-Learn, among others. It is open source and available under the MIT License, encouraging contributions and collaboration from the community.

HoopsForecast - NBA Performance Predictor

Stocklytics offers a comprehensive solution for real-time stock analysis, leveraging advanced technical indicators, price forecasting, and news sentiment analysis. This innovative platform is designed for investors and traders who seek to make informed decisions based on data-driven insights. Key features include the integration of technical indicators that provide a detailed view of market trends, enabling users to anticipate price movements effectively. Additionally, the platform's price forecasting capabilities are powered by sophisticated algorithms that predict future stock prices with high accuracy. <ul><li>Real-time stock analysis</li><li>Advanced technical indicators</li><li>Price forecasting</li><li>News sentiment analysis</li></ul> By analyzing news sentiment, Stocklytics helps users understand market sentiment and its potential impact on stock prices. This feature is particularly valuable for gauging the mood of the market and making timely investment decisions. Whether you are a seasoned trader or a novice investor, Stocketics provides the tools and insights needed to navigate the complexities of the stock market with confidence.