Brodie Lee

Georgetown University. I build products end-to-end — from sizing up the market and the data that's available, to shipping the software and instrumenting whether it works.

Field Brief front page: dark editorial layout with story clusters and a geopolitical risk index

Field Brief Defense-tech news intelligence

A personalized news feed for the defense and government-tech niche. Ingests 15 curated outlets over RSS, extracts entities and topics with an LLM pipeline, clusters related articles into stories with cited digests, and re-ranks the feed around what each user follows. A companion dashboard tracks DoD contract awards (USAspending), SAM.gov opportunities, and defense-sector market data.

The bet: defense professionals are a narrow, high-intent audience underserved by general news products — personalization plus procurement signals is the wedge.

Next.jsSupabaseLLM pipelineEmbeddingsUSAspending / SAM.gov
Rounds app: personal ranking of Boston cocktail bars with loved/fine ratings

Rounds Social nightlife discovery

Rate the bars you visit, compare them head-to-head, and build a shareable personal ranking — powered by a per-user Bayesian Bradley–Terry model so sparse comparisons don't produce overconfident orderings. Friends-first social layer (DMs, group chats, polls, shared reviews) shipped before any public graph.

Product decisions are documented as they were made: 10 ADRs including competitive assessments of event-data providers, a deliberately market-scoped beta (Boston/Cambridge), cohort-isolated rankings, and PostHog instrumentation from day one.

React Native / ExpoFirebaseBayesian rankingPostHog
HealthyHoyas landing page: nutrition tracking built for Georgetown dining halls

HealthyHoyas Campus nutrition tracking

Nutrition tracking that actually works for Georgetown dining halls. A Python scraper ingests the daily HoyaEats menus and per-item nutrition facts for every dining location; the app turns them into a two-tap food diary with a nutrition dashboard.

Generic trackers fail on campus food — it isn't in their databases. Building the data pipeline (scraper → API → app) for one known, reachable user base made the product viable where MyFitnessPal isn't.

Next.jsSupabasePython scraper3-repo pipeline
ATLAS terminal: seven AI agents deliberating a trade with citations and outcome-weighted votes

ATLAS Multi-agent AI research system

A trading-research collective of AI agents with specialized roles (market analyst, news analyst, quant researcher) that debate strategy under evidence-first rules — every claim must cite a source. An accountability ledger adjusts each agent's voting power based on outcomes, and agents compete in role elections. Executes against an Alpaca paper-trading account with strict risk guardrails.

A systems-design exercise in making LLM agents accountable: merit-weighted governance instead of trusting any single model's output.

PythonClaude APIMulti-agentAlpaca25k+ LOC
Photon editor with a photo open: layers panel, filters, and drawing tools

Photon In-browser photo editor

A single-page photo editor with crop/resize/filters, drawing tools, and one-click background removal — 100% client-side. No accounts, no uploads, no server processing.

Zero-friction consumer utility: the entire product is one HTML file, and privacy (nothing leaves the browser) is the differentiator.

Vanilla JSCanvasClient-side ML