StudyOps — AI Engineering study path

StudyOps is a personal project created to organize my AI Engineering study path and turn learning into visible progress. Built entirely in Codex, it also works as a lab for exploring commands, AI-assisted workflows, and MCP integrations with platforms such as Supabase.

Anyone can sign in and build their own knowledge path. The app tracks fundamentals, study cycles, practical tasks, notes, and evidence, connecting what each person studies with real deliverables that can grow into portfolio projects.

The design was conceived as a command center for study: a dark, operational interface inspired by maps, missions, and exploration. Instead of feeling like a generic task list, it shows what is being studied, which mission is active, what evidence has already been produced, and what comes next. The visual identity combines disciplined routine with exploration to make technical progress easier to follow.

The content comes from a curated AI Engineering path that combines original documents, portfolio project planning, and public references from GitHub and Hugging Face. Each reference must connect to a foundation, a practical task, or concrete evidence. The core loop is to study a concept, apply it in a small implementation, connect it to a larger project, record the result, and decide what comes next.