I build projects in Python and TypeScript, with a focus on backend APIs, visual workflows, and applied machine learning.
My repositories explore how software moves from an idea to something that can be run, tested, and improved. I'm currently strengthening their validation, documentation, and automated checks.
| Project | What it explores | Stack |
|---|---|---|
| Solana Data Aggregator | Combining upstream data, validating responses, caching snapshots, and streaming updates | TypeScript · Fastify · Redis · Socket.IO |
| Flowvy | A visual editor for LLM and media workflows, with graph validation and JSON import/export | Next.js · React Flow · Prisma · Trigger.dev |
| Grocery Store | A catalog, authenticated carts, orders, and optional checkout integrations | Python · Flask · SQLAlchemy · JWT |
| Forest Fire FWI Predictor | An educational regression model served through a validated Flask form | Python · scikit-learn · Flask |
| Student Performance ML Project | Data ingestion, preprocessing, model comparison, and a prediction interface | Python · pandas · scikit-learn · Flask |
- Making projects easier to run from a clean checkout.
- Writing regression tests for the edge cases that matter.
- Improving API reliability and workflow validation.
- Documenting design choices, limitations, and reproducible results.
Languages: Python, TypeScript, JavaScript, C++
Backend and data: Flask, FastAPI, Fastify, SQLAlchemy, Redis, PostgreSQL
Web and workflows: React, Next.js, React Flow, Prisma
Quality: Git, pull requests, GitHub Actions, pytest, Jest
Browse the project READMEs for setup instructions, implementation details, and current limitations. Bug reports with clear reproduction steps are welcome in each project's issue tracker.