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juanfernandez13/README.md

Juan Fernandez

I'm a computer engineer from IFCE, currently working as a Mid-level Analyst at the Court of Auditors of the State of Ceará (TCE Ceará) and as a software engineer at Hunni Dev. Day to day I wire AI APIs — OpenAI, Gemini, Claude, Azure — into real products with Python and TypeScript, from retrieval-augmented generation to process automation, and I've also worked on front-end with Next.js/React, mobile with Flutter and React Native, and distributed systems in Java during my degree. Based in Fortaleza, Brazil.

LinkedIn · juanfernandez.app@gmail.com


AI & Automation — Python, TypeScript, OpenAI API, Gemini, Claude, Azure, N8N, RAG, MCP
Frontend — Next.js, React, TypeScript, Tailwind CSS
Backend — Node.js, Express, PostgreSQL, SQLite, Redis, Prisma ORM
Mobile — React Native, Flutter
E-commerce — Shopify, WooCommerce
Testing & Scraping — Playwright, Puppeteer, Cypress, Cheerio, Crawlee


Python TypeScript Next.js Claude Node.js


Overall profile metrics


Isometric contribution calendar Most used languages

Projects

CourseGen AI converts course planning documents (.docx) into Moodle backup packages (.mbz) ready to import, using Gemini to extract the course structure; to get around Vercel's 4.5MB upload limit, uploads go straight from the browser to Blob Storage using server-signed URLs.

python_agent is a Q&A backend that retrieves context from a Markdown knowledge base before calling the LLM, built with FastAPI and Pydantic; citations only make it into the response if the section title actually appears in the generated text, which keeps the source attribution honest.

chat_offline_ppd delivers messages instantly when the recipient is online and queues them when offline; the interesting call was hiding JMS/ActiveMQ behind an RMI interface, so the client never talks to the queue directly, only calls remote methods.

Classificacao-folhas classifies leaves from 32 species out of ~1,900 images using classical computer vision instead of deep learning — k-means segmentation, ten shape/texture descriptors reduced via PCA (93% of variance in 4 components), and SVM, which kept the pipeline interpretable and lightweight.

Pinned Loading

  1. aprendoai_backv2 aprendoai_backv2 Public

    TypeScript 1 1

  2. Classificacao-folhas Classificacao-folhas Public

    Python

  3. CourseGen-AI CourseGen-AI Public

    JavaScript

  4. sockets-ppd sockets-ppd Public

    Java

  5. trabalho_CG trabalho_CG Public

    Python

  6. bot-moodle bot-moodle Public

    JavaScript