Shortest Path Comparator
JavaScript
Node.js
HTML/CSS
Express.js
Canvas API
- Interactive Algorithm Visualization: Created an engaging web application that brings computer science theory to life by visualizing Dijkstra and Bellman-Ford algorithms in real-time, making complex concepts accessible through interactive demonstrations.
- Intuitive Graph Editor: Engineered a sophisticated Canvas-based interface that transforms algorithm learning into an interactive experience, allowing users to create, modify, and experiment with custom graphs through seamless drag-and-drop functionality.
- Performance-Optimized Backend: Architected a robust Express.js API that delivers lightning-fast algorithm comparisons, providing detailed performance metrics and enabling educational insights into algorithmic complexity.
- Advanced Implementation: Developed production-quality algorithm implementations with binary heap optimization and negative cycle detection, demonstrating deep understanding of data structures and algorithmic efficiency.
Puter Initial Setup Wizard
JavaScript
Node.js
RESTful
jQuery
JSON
Tailwind CSS
- Enterprise-Grade Setup Experience: Designed an intuitive multi-step wizard that transforms complex system initialization into a streamlined user experience, ensuring secure and reliable deployment for enterprise environments.
- Versatile Domain Management: Engineered a flexible configuration system that seamlessly handles diverse deployment scenarios—from local development to production environments—supporting custom domains, localhost, and NIP.io configurations.
- Security-First Architecture: Built a modular, scalable system with robust token-based authentication and intelligent configuration management, ensuring data security and system reliability from the ground up.
- Intelligent Error Handling: Implemented sophisticated automated domain validation with proactive error detection and self-recovery mechanisms, minimizing deployment friction and maximizing system uptime.
LLM Evaluation Platform
Python
TypeScript
SQL
React
HTML/CSS
Docker
- Comprehensive LLM Testing Platform: Architected a sophisticated full-stack application that empowers researchers and developers to conduct rigorous A/B experiments, providing scientific methodology for evaluating and improving AI model performance.
- Scalable Data Architecture: Designed a hybrid SQL/NoSQL database system that effortlessly scales from prototype to production, handling complex experimental data structures while maintaining optimal query performance.
- Performance: Achieved a remarkable 30% performance improvement through strategic API optimization and intelligent database indexing, ensuring responsive user experience even with large-scale experimental datasets.
- User-Centric Interface: Crafted an elegant, responsive frontend that transforms complex experiment configuration into an intuitive workflow, featuring dynamic data visualization that makes insights immediately actionable.
- Production-Ready Deployment: Implemented containerized architecture with Docker, enabling one-click deployment and horizontal scaling to meet growing research demands across diverse computing environments.
Stock Analysis and Automation
TypeScript
Tailwind CSS
Next.js
Preact
SWC
- Modern Web Architecture: Built a cutting-edge financial analysis platform using Next.js, React, and TypeScript, delivering a premium user experience with a cohesive Tailwind-based design system that ensures consistency across all trading interfaces.
- Performance Optimization: Achieved lightning-fast load times through advanced optimization techniques including SWC compilation, Preact production builds, and intelligent bundle splitting—delivering real-time market data without compromise.
- Developer Experience: Established a world-class development workflow with pnpm, ESLint, and structured Git practices, enabling rapid iteration and seamless collaboration across development, staging, and production environments.
- Cross-Platform Compatibility: Ensured universal accessibility through PostCSS and Autoprefixer integration, while ContentLayer provides flexible content management for dynamic market insights and analysis reports.
Market Anomaly Detection
Python
Pandas
XGBoost
NumPy
Streamlit
Plotly
- AI-Powered Market Intelligence: Developed a sophisticated machine learning system using Python, Pandas, and XGBoost that achieves an exceptional 99% accuracy in predicting market crash probabilities, providing invaluable early warning signals for risk management.
- High-Performance Analytics Dashboard: Created an enterprise-grade visualization platform with Streamlit and Plotly that delivers real-time market insights to 500+ daily users with sub-second response times, making complex financial data instantly actionable.
- Production-Grade Infrastructure: Engineered a robust, fault-tolerant data processing pipeline achieving 99.7% uptime with optimized model persistence, delivering 3x faster inference speeds for time-critical trading decisions.
- Proven Market Impact: Demonstrated exceptional predictive capability by successfully identifying 8 out of 10 significant market events during testing phase, with investment strategies that protect client portfolios and deliver an average 12% loss prevention.
Bank Churn Prediction
Python
Jupyter Notebook
Streamlit
Llama 3.1
Groq
Vercel
- Advanced Predictive Analytics: Developed a sophisticated machine learning solution leveraging a 30,000+ customer dataset to predict banking churn with high accuracy, enabling proactive customer retention strategies and reducing revenue loss.
- AI-Powered Customer Engagement: Integrated cutting-edge Llama 3.1 and Groq technologies to create an intelligent system that automatically generates personalized retention emails, transforming raw predictions into actionable customer engagement strategies.
- End-to-End ML Pipeline: Architected a complete machine learning workflow encompassing feature engineering, data normalization, model training, and hyperparameter optimization across 5 different LLM models, demonstrating comprehensive understanding of the ML lifecycle.
- Production-Ready Deployment: Built a user-friendly Streamlit application deployed on Vercel, providing banking professionals with an intuitive interface to assess customer risk and implement targeted retention campaigns in real-time.
BlockCarbon Credits
Python
Google Earth Engine API
HTML/CSS
- Environmental Impact Technology: Collaborated on an MIT-commissioned project to develop an innovative web application that accurately calculates carbon credits for geographic areas using Google Earth Engine API, contributing to sustainable environmental monitoring and carbon offset verification.
- Advanced Data Augmentation: Engineered sophisticated hyperparameter optimization and data manipulation techniques to transform a limited dataset of under 200 images into a robust training foundation, maximizing model performance despite resource constraints.
- Collaborative Model Development: Led cross-functional team efforts to implement and optimize cutting-edge deep learning architectures including ResNet and EfficientNet, employing innovative training methodologies to achieve superior environmental detection accuracy.
- Model Performance Analytics: Developed comprehensive evaluation frameworks using confusion matrices and statistical analysis to identify overfitting, optimize sampling strategies, and ensure reliable carbon credit calculations for real-world deployment.
Classifying Budgerigars
Python
Jupyter Notebook
ResNet
EfficientNet
Machine Learning
Computer Vision
- Advanced Image Classification: Developed a sophisticated machine learning system for automated budgerigar classification, demonstrating expertise in computer vision and species identification through cutting-edge deep learning techniques.
- Data Optimization: Engineered innovative hyperparameter optimization and data augmentation strategies to maximize model performance from a limited dataset of under 200 images, showcasing ability to achieve robust results despite resource constraints.
- Collaborative Deep Learning Development: Led team initiatives to implement and compare state-of-the-art neural network architectures including ResNet and EfficientNet, employing unique training methodologies to optimize classification accuracy across different model frameworks.
- Comprehensive Model Analysis: Developed rigorous evaluation protocols using confusion matrices and statistical analysis to identify overfitting patterns, optimize sampling strategies, and ensure reliable performance metrics for production-ready deployment.
Bulba Code Rating Multiturn
Python
C++
Java
LLM Evaluation
AI Training
Code Analysis
- Enterprise AI Enhancement: Delivered a high-impact client project in partnership with Outlier AI, focusing on advanced LLM evaluation and optimization to improve artificial intelligence capabilities for enterprise-level code generation and analysis applications.
- Exceptional Performance Improvement: Achieved a remarkable 50% increase in LLM performance through systematic analysis and critique of model responses across code generation and text production tasks, demonstrating expertise in AI model optimization and evaluation methodologies.
- Advanced Technical Assessment: Evaluated AI responses against complex software engineering prompts at college bachelor's level and beyond, working across multiple programming languages (Python, C++, Java) to ensure comprehensive model understanding and accuracy.
- Multimodal AI Enhancement: Significantly improved model capabilities in both image detection and prompt comprehension through rigorous testing and feedback loops, contributing to more robust and reliable AI systems for real-world applications.