I build full-stack products — APIs, graph systems, game backends, data pipelines, and AI-assisted learning tools.
- 🔭 Currently working on language learning (Lingo) and Pokémon tooling (Pokémon Center)
- 🌱 Currently learning distributed systems, AWS, and deeper graph algorithms
- 💬 Ask me about Go/Rust backends, async workers (Python · JS · TS), data science, or AI in production
- ⚡ Fun fact: I like shipping the same idea across stacks — Go/Rust API + Python/TS workers + Next.js UI is my default combo
| Project | What it does | Stack |
|---|---|---|
| Lingo | Spaced-repetition language learning — decks, review sessions, exercises, habits | Go · Python · PostgreSQL · Redis · Next.js |
| Pokémon Center | Pokémon catalog, teams, async analysis jobs, PokeAPI ingestion | Go · Python · PostgreSQL · Redis · Next.js |
| VaultTrace | Crypto mixer simulation + forensic graph tracing (BFS, DFS, A*, Dijkstra) | Go · Next.js |
| InvestGraph | Investment recommendations via graph collaborative filtering (Jaccard) | Go · Python · Next.js |
| CampusWorld | Persistent social Minecraft server — whitelist, guilds, trust engine, web dashboard | Java (Paper) · Spring Boot · Next.js |
| UFPB Study | Deterministic exercise platform for UFPB courses — seeds, step-by-step solutions, AI tutor | Next.js · OpenAI |
SRS language-learning platform: Go HTTP API, Python workers (Redis queue, NLP/LLM jobs), PostgreSQL, and a Next.js App Router frontend for review flows, decks, exercises, and habit tracking.
Monorepo for Pokémon data and competitive tooling — PokeAPI catalog ingestion, regional dex, team analysis with async Python workers, and companion apps.
Wallet controller with a Compliance Lab: simulate crypto mixing on transaction graphs, then trace flows with classic graph search — attack vs. defense in one demo.
Graph-based investment recommendations: if two portfolios overlap, missing assets become explainable suggestions — Jaccard similarity on the portfolio graph, neighbor by neighbor.
Persistent social layer on Minecraft: Paper plugin (whitelist, invites, claims), Spring Boot API (trust engine, analytics), and a Next.js site for profiles, guilds, and dashboards.
Study platform for UFPB disciplines: 29 topics, reproducible exercise seeds, LaTeX + SVG, shareable links, exam mode, PDF export, and an AI Explain tutor powered by LLMs.
Languages & runtimes
Backend, workers & data
Frontend
DevOps
Also: data science · graph algorithms · AI/LLM integration · async workers in Python, JavaScript, and TypeScript
