I'm an AI-native product builder and Drexel Entrepreneurship grad (June '26) building automated pipelines, AI-powered tools, and systems that actually ship. Previously at Aramark, Satisfi Labs, and BevSpot.
700+
automated videos shipped
OpenAI x Handshake
Codex finalist
Live apps
TSA + World Cup systems
Recognition
OpenAI and Handshake selected my project, TSA Tracker, as a finalist in the 2026 Codex Creator Challenge. TSA Tracker is a live airport utility that provides travelers with real-time checkpoint wait times, airport-specific guidance, and recent history. My goal with this project is to allow travelers to make informed, data-driven decisions rather than guessing.
Toolchain rail
Hover case studies to isolate related tools
Examples of live shipped projects and demos, including inputs, transformations, checks and outputs
Case 01
Output verified
A real-time TSA wait-time tracking web app. Built a Python/Flask backend that scrapes and normalizes live checkpoint data, with a mobile-first frontend optimized for travelers checking security times before or during a trip.
10.7K
users in the first 30 days
Case 02
Output verified
An end-to-end automated Python pipeline that polls MLB’s StatsAPI in real time for home runs, downloads broadcast highlights, compiles vertical Shorts via FFmpeg, and auto-uploads to YouTube with Statcast-enriched metadata. 700+ videos published, 500K+ views on @SportsTrakker.
700+
automated videos published
Case 03
Output verified
A live FIFA World Cup 2026 app for finding soccer bars, fan festivals, public screenings, and match-specific watch spots by city, with map/list sync, fan signals, source links, and trust labels.
10
Boston spots live for Match 45
Case 04
Output verified
A live PDF trust scanner that finds invisible text, off-page content, tiny text, metadata issues, and prompt-injection style instructions before people or AI systems rely on the document.
Live
inspectable trust scanner demo
Case 05
Output verified
A live GitHub Pages demo for a rules-based sports market intelligence system that compares Kalshi pricing against sportsbook consensus, estimates positive EV, and applies hard risk controls before any action is allowed.
Live
public demo with hard risk rules
Case 06
Build track
An intelligent audio engineering platform that analyzes, beatsyncs, and automixes tracks using advanced DJ transitions, powered by a custom Python backend with FFmpeg and Pydub.
FFmpeg
automation backend shipped
A live view of project reach, shipped demos, traffic, and the automation signals behind the portfolio.
Live stats monitor
Live and portfolio-backed signals for reach, traffic, and shipping velocity.
HomerTrakker
Source: YouTube channel API
TSA Tracker
Source: live traffic endpoint
Shipping velocity is scoped to live products and shipped demos, not speculative ideas.
I start with a real signal, turn it into a repeatable workflow, add checks, and ship something people can inspect.
Input
Find the API, dataset, workflow, or user behavior that actually drives the system.
Transform
Turn the repeatable path into scripts, agents, product flows, or integrations.
Check
Add review states, dedupe logic, risk rules, health checks, and plain-English traces.
Output
Release a demo, dashboard, video, live app, or case study a reviewer can inspect.
Product Operations Associate
Revenue Coordinator
Premium Supervisor, Citizens Bank Park
Co-Founder & Owner
Where I fit
I am a Drexel Entrepreneurship grad building at the edge of product, operations, and AI tooling. I am most useful when a team needs someone who can understand the workflow, build the system, and ship evidence quickly.
Co-op experience across BevSpot, Satisfi Labs, and Aramark gave me the operating context; the projects above show the shipping loop.
Map messy workflows, write the product shape, and turn repeated manual work into scoped systems.
Build the scripts, agents, APIs, and no-code glue that make the workflow repeatable.
Release live demos, dashboards, videos, and case-file evidence instead of stopping at concepts.
