Hackathons taught me what a classroom can't: spotting a real problem, scoping it to a weekend, and shipping something that works in front of judges before the clock hits zero. Across events like Willhacks, HackVH, Tech4Good, Code4Good, and HackJPS, the pattern that won was always the same — a genuine problem, a simple ML or automation core, and a demo that just works. Here are the four builds that prove it. Click any card to flip it.
Point your camera at a meal; a machine-learning model tells you which food groups it's missing.
Built a Python/TensorFlow image-classification model that analyzes photos of meals and flags missing nutrients across the five food groups — a practical tool aimed at fighting diet-driven obesity. As a 4-person team, we trained the model, wrapped it in a usable app, and pitched it live to United Nations judges, taking 1st place out of more than 40 teams. My first proof that an ML model and a clear story beat a complicated demo.
Texts you the moment a vaccine appointment opens in your zip code — built when appointments were nearly impossible to find.
During the height of the vaccine shortage, appointments vanished within minutes of appearing. BoostMe is a Python/Twilio service that continuously monitored appointment availability by zip code and sent instant SMS alerts when a slot opened. Simple architecture, real stakes: automation applied to a problem people were refreshing browser tabs over. Took 2nd place at WilHacks 2.0.
A public-speaking coach that catches your "um"s and "like"s — built to make high schoolers more confident presenters.
A speech-analysis tool that listens to you practice and flags filler words — the "um"s, "uh"s, and "like"s that undermine a presentation — so speakers can see their habits and train them away. Aimed at students who dread presenting. It earned a winner badge, and it's the project that taught me a tool people actually want to use starts from a problem you've personally felt.
An assistive-vision app that describes the world out loud for visually impaired users, in real time.
An accessibility app that pairs OpenCV with TensorFlow to caption a live camera feed — describing surroundings aloud so visually impaired users can navigate more independently. Real-time image captioning on a hackathon deadline forced hard trade-offs between model size, latency, and accuracy: my first genuine lesson in deploying ML under constraints, not just training it.
Weekend builds are where I learned to move quickly without cutting the corners that matter. If that's the kind of energy your team runs on, let's talk.