01
SCENR
A collaborative trip-intelligence platform: everyone's scattered trip media becomes one shared story, plus a personalized version for every friend who was there.
Product manager leading a 10-person team. SCENR turns a friend group's scattered camera rolls into a shared story: one person creates a trip, everyone uploads to a link, and SCENR analyzes the trip once to group shots into shared moments, surface the strongest, and flag what's missing. It then produces a balanced Group Cut plus a personalized Trip Pack for each participant (their own reel, photo dump, Story set, and a Photos of You collection). Instead of an editing timeline, users steer their version with direct-intent controls like More Me ↔ More Group and Candid ↔ Polished. Shipping on iOS and Android with Flutter, a Node.js/Fastify and PostgreSQL backend, Cloudflare R2 storage, Redis/BullMQ processing, Python computer-vision Moment Intelligence, Essentia music analysis, and FFmpeg.
- Flutter
- Node.js / Fastify
- PostgreSQL
- Cloudflare R2
- Redis / BullMQ
- Computer Vision
- FFmpeg
02
NINOS
An automated security team, for startups too small to afford one.
AI Technical Lead directing a team of 3 developers on a University of Calgary ENGG 503/504 entrepreneurial capstone. NINOS is an AI-driven security product for early-stage startups building with AI coding tools: a red team agent attacks the app the way a real attacker would, and a blue team agent produces the fix, or a policy guardrail when a fix can't be applied automatically, explained in plain English. Currently in design: owning technical direction across AI infrastructure, system design, and tech stack selection, and architecting the application and CLI, including agent orchestration, the vulnerability data model, and the detection and fix pipelines, behind a modular LLM provider layer that keeps the agents independent of any single model vendor.
- AI Agents
- LLM Orchestration
- Application Security
- PostgreSQL / pgvector
- Node.js / TypeScript
03
FitPic
A fashion accountability platform: daily fits, partner streaks, and a vision model reading your wardrobe back to you.
A computer-vision pipeline on Groq's Llama 4 Scout vision-language model extracts structured outfit attributes from user images against an enforced JSON schema, retrying invalid outputs and storing any that stay unreconciled as null, with 98% schema-valid output and 83% tag agreement over roughly 200 images, backed by PostgreSQL via Supabase Realtime. Behavioral and color trends surface in a personal analytics dashboard, secured behind a serverless Node.js/Vercel endpoint with JWT validation and server-side API-key management.
- React
- Supabase
- Groq API
- Llama 4 Vision
- Node.js
- Vercel
04
NBA Betting
Odds Analyzer
A dual-mode over/under predictor that picks its own best model, every query.
A train-on-demand path trains and evaluates five classifiers (XGBoost, Random Forest and others) per query and selects the best by validation ROC-AUC, alongside a fast path serving the best model logged in MLflow. Model selection is backed by MLflow experiment tracking and a versioned model registry, logging params and ROC-AUC/PR-AUC per run for reproducible comparison across retrains.
- Python
- Scikit-Learn
- XGBoost
- MLflow
- NBA API
