AI-focused projects and open source contributions showcasing practical implementation of agentic AI, conversational systems, and large language model applications.
2024•AI Research•Active Development
Agent Boss AI Radar System
Developed an intelligent AI research monitoring system that automatically tracks, analyzes, and summarizes emerging AI research papers and trends. Uses advanced web crawling and LLM summarization to provide real-time insights into the AI landscape.
Created a comprehensive template and framework for building multi-agent AI systems using CrewAI. Provides reusable components for orchestrating multiple AI agents to work together on complex tasks.
Key Results
• Multi-agent orchestration
• Reusable templates
• Task coordination
• Scalable architecture
• Open source contribution
Technology
Python,CrewAI,LangChain,OpenAI,FastAPI,Docker
Impact:Accelerated agent development
Team:Solo project
2024•Conversational AI•Production Ready
Conversational AI Agent Platform
Built advanced conversational AI agents capable of handling complex multi-turn conversations with context awareness. Features include memory management, intent recognition, and seamless human handoff capabilities.
Developed a specialized AI agent for dental practice management, automating appointment scheduling, patient communication, and treatment recommendations. Integrates with existing dental practice management systems.
Key Results
• Appointment automation
• Patient communication
• Treatment recommendations
• Practice integration
• HIPAA compliance
Technology
Python,OpenAI,FastAPI,PostgreSQL,Twilio,Docker
Impact:Streamlined dental operations
Team:Solo project
2024•AI Demonstration•Educational
Intelligent Agent Demo Platform
Created a comprehensive demonstration platform showcasing various AI agent capabilities including task automation, decision making, and human-AI collaboration patterns. Serves as a learning and testing environment for agent technologies.
Developed a framework for building agentic applications that can autonomously perform complex tasks by breaking them down into manageable subtasks and coordinating multiple AI agents to achieve goals.
Key Results
• Autonomous task execution
• Subtask decomposition
• Multi-agent coordination
• Goal-oriented behavior
• Framework architecture
Technology
Python,LangChain,OpenAI,FastAPI,PostgreSQL,Docker
Impact:Simplified agentic development
Team:Solo project
2024•AI Protocols•Demo
Model Context Protocol (MCP) Demo
Implemented demonstrations of the Model Context Protocol, showcasing how AI models can securely access external tools and data sources. Includes examples of tool integration and context management.
Key Results
• Secure tool access
• Context management
• Protocol implementation
• Integration examples
• Security best practices
Technology
JavaScript,Node.js,MCP,OpenAI,Express,Docker
Impact:MCP adoption showcase
Team:Solo project
2024•Conversational AI•Prototype
Chatterbox AI Prototype
Built an advanced conversational AI prototype featuring natural language understanding, context retention, and personality adaptation. Designed for engaging and human-like interactions across various use cases.
Key Results
• Natural language understanding
• Context retention
• Personality adaptation
• Multi-turn conversations
• Engaging interactions
Technology
Python,OpenAI,LangChain,Streamlit,Redis,Docker
Impact:Enhanced conversational AI
Team:Solo project
2024•AI Education•Open Source
Anthropic Claude Cookbook Collection
Curated and contributed to a collection of Jupyter notebooks showcasing effective ways to use Claude AI models. Includes recipes for various AI tasks, prompt engineering techniques, and best practices.
Implemented and experimented with DeepSeek V3 language model capabilities, including fine-tuning, inference optimization, and integration patterns. Explored advanced features and performance characteristics.
Developed starter applications using Google AI Studio, showcasing various AI capabilities including text generation, image analysis, and multimodal interactions. Serves as templates for AI application development.
Key Results
• Text generation
• Image analysis
• Multimodal interactions
• Application templates
• Google AI integration
Technology
TypeScript,Google AI Studio,React,Node.js,Docker,GitHub
Impact:AI application templates
Team:Community contribution
2024•Large Language Models•Open Source
Llama Model Utilities
Created utilities and tools for working with Meta's Llama language models, including inference optimization, model management, and integration helpers. Focused on practical implementation patterns.
Key Results
• Inference optimization
• Model management
• Integration helpers
• Implementation patterns
• Open source tools
Technology
Python,Llama,Transformers,PyTorch,Docker,GitHub
Impact:Llama model accessibility
Team:Community contribution
2024•AI Research•Production
Agent Boss Production System
Production deployment of the Agent Boss AI Radar system with enhanced scalability, reliability, and monitoring capabilities. Includes automated deployment pipelines, error handling, and performance optimization.
Developed a Retrieval Augmented Generation (RAG) system integrated with Azure Service Bus for asynchronous processing. Enables scalable document retrieval and context-aware AI responses for enterprise applications.
Key Results
• RAG implementation
• Service bus integration
• Asynchronous processing
• Vector search
• Enterprise scalability
Technology
Python,Azure Service Bus,OpenAI,LangChain,Vector DB,Docker,FastAPI
Impact:Scalable RAG architecture
Team:Solo project
2024•ML Operations•Utility Library
HuggingFace Model Operator
Created operators and utilities for managing HuggingFace model deployments, including model loading, inference optimization, and integration patterns. Simplifies working with transformer models in production environments.
Built a specialized client for Comet ML experiment tracking, enabling comprehensive monitoring of AI/ML experiments including hyperparameters, metrics, models, and datasets. Facilitates reproducible research and model comparison.
Developed a framework for transitioning AI models from research to production, including deployment pipelines, monitoring, and scaling strategies. Focuses on making AI model deployment faster and more reliable.
Collection of Jupyter notebooks documenting AI research, experiments, and implementations. Includes model evaluations, prompt engineering experiments, data analysis, and reproducible AI workflows.
Created a comprehensive evaluation framework for AI software and tools, providing structured criteria for assessing AI platforms, models, and frameworks. Helps organizations make informed decisions about AI technology adoption.