LaunchPilot

• LaunchPilot is a two-time winning project at Hack Canada 2026 built to move a product from initial brief and codebase context into a supervised launch workflow.
• The stack combines a Next.js App Router frontend, FastAPI backend, SQLAlchemy, Alembic, and Postgres for project state, approvals, activity, and stage outputs.
• The agent layer uses Backboard-backed research, positioning, and execution agents with persistent threads and memory snapshots so each stage can reuse structured context from prior runs.
• It ingests project brief and GitHub context, maps competitors and pain points, generates ICP and messaging options, builds a 7-day execution plan with KPIs, and prepares personalized outreach batches for approval before send.

WebsiteSourceDevpost

FactorAtlas

• A full-stack portfolio intelligence platform built with Next.js, TypeScript, Tailwind CSS, shadcn/ui, Recharts, FastAPI, SQLAlchemy, Pydantic, PostgreSQL, and Docker Compose.
• It computes deterministic portfolio metrics like annualized volatility, beta, Sharpe ratio, and correlation matrices using pandas, numpy, scipy, statsmodels, and networkx.
• The architecture is split into modular portfolio, market data, quant, graph, and AI services that support real-time yfinance ingestion, event relevance scoring, and scenario stress testing.
• AI prompts are grounded in structured analytics for traceable explanations, while caching, async endpoints, and type-safe contracts address performance and integration complexity at the current scaffold stage.

Source

Discrete-Time Markov Chain for Market Regime Forecasting

• A modular Python application that models daily equity return regimes (down/flat/up) using a first-order Markov chain and forecasts next-day state probabilities from historical price data.
• It takes and cleans CSV data, computes returns, discretizes regimes, builds a row-normalized transition matrix, and outputs conditional next-state probabilities based on the observed regime.
• Includes a CLI report tool, a Flask dashboard with threshold tuning and Monte Carlo simulation, plus unit tests for transition/state logic.

Source

Heat Mapping $5,000+ Thefts Around UTSG

• I got my stuff stolen at the Athletic Centre at UofT and almost lost most of my valuables, so I decided to make a project on theft around UTSG.
• A full-stack crime intelligence web app that maps Toronto Police theft-over-$5,000 incidents around the UofT St. George campus.
• It runs a Python/FastAPI pipeline that filters records by campus geospatial boundaries, normalizes data in SQLite, and serves a clean API.
• Includes a Next.js/TypeScript frontend with an interactive OpenStreetMap heatmap, live summary metrics, and incident sample tables.

WebsiteSource

Stochastic Risk Modeling: Gambler's Ruin Simulation

• Built a Gambler's Ruin simulation platform in Python that combines Monte Carlo experimentation with closed-form probability analysis.
• It validates stochastic outcomes against theory and is organized as a reusable package (simulation, analytics, visualization, cli, and webapp).
• It supports up to 100k trials per run with both command-line and interactive web workflows using Flask and Streamlit.
• I implemented convergence diagnostics, empirical-theoretical error tracking, and an interactive Plotly dashboard for reproducible analysis.

Source