AI and full-stack developer with a background spanning both technology and culinary leadership. Combines hands-on experience in Python, machine learning, and web development with years of working in fast-paced, high-accountability environments. Known for adaptability, disciplined execution, and a strong problem-solving mindset.
This website is serving as a place to learn, bringing personal and class projects to life and is a continuous work in progress.
Foundations
Technical
- Python
- NumPy, Pandas, Matplotlib, Seaborn, spaCy, NLTK (Natural Language Toolkit), scikit-learn, SciPy, Flask, Django, FastAPI
- JavaScript
- Node.js, Express.js
- Database Theory and Design
- Oracle SQL, SQLite
- Operating Systems
- Windows, macOS, Linux, Raspberry Pi
- Network Basics
- OSI Model, TCP/IP, VPS (Virtual Private Server) Management, NGINX, PM2 (Process Manager), Bash Shell
It's Me!
Projects (Works in Progress)
onebadev.com - Chat Bot: BadBot
Originally developed as a rule-based Python chatbot for a Natural
Language Processing class, this assistant was designed to handle
predefined prompts, deliver custom responses, and guide users
through basic questions and answers. The current version has been
rewritten in JavaScript and has laid the groundwork for LLM
integration using Gemma3:1b, node.js, express.js, and finally
integrating LangChain and Chromadb,
creating a more responsive agent.
Journal | Notes:
- Jan 14, 2026: The site itself has been up and running (and will continue to be), but after deploying BadBot to my VPS I have encountered a performance bottleneck. While responses were fast on my personal PC, the minimal, CPU only server (no GPU) has been struggling under the load. The model, Gemma3:1b, is too heavy for the available hardware, pushing the CPU to max out causing response times to rise. To hopefully solve this, I am currently downsizing the LLM to a leaner Gemma3:270M and reimplementing a lightweight, rule-based system to handle common questions quicker. In this hybrid setup, the AI model is only invoked for more complex queries, helping balance model capability with hardware constraints, keeping the site fast and responsive. Barring any issues with the updated model, (or anything else) upcoming improvements include lightweight in-session memory to further improve flow while maintaining hardware efficiency.
AI Agent Deep Learning Capstone
A collaborative deep learning capstone project focused on building an AI-powered process automation agent. We began with custom NLP tools and Python-based logic to interpret language input and automate workflows like e-mail responses, task creation, and calendar scheduling. The project later transitioned to the n8n automation platform, enabling faster development and integration of modular workflows without rebuilding core logic from scratch.
GitHub LinkAI Resources and Final Capstone
A proof of concept group project to validate that a lightweight, edge-deployable AI system running on Raspberry Pi can detect fire in real time and transmit location-tagged alerts via telemetry, demonstrating the feasibility of using the system in aerial or remote wildfire surveillance.
GitHub LinkBaseball Analysis System
This Python project started as a way to study and practice for my data analysis class at Houston City College and focuses on the analysis of pitcher and batter trends using historical MLB datasets. It leverages pandas and numpy for data manipulation, while matplotlib and seaborn generate visual insights into pitch selection frequency, dominant pitch types, and annual patterns. The system currently supports CLI interaction and player-specific breakdowns.
GitHub Link