Initial project structure: directories, empty files, requirements.txt, and logo

.cursor/rules/ai.mdc

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+---
+alwaysApply: true
+---
+PROJECT CONTEXT
+Stack: Docker-based Python backend + simple frontends
+Version control: Gitea (commit after tasks, no auto-push)
+Environment: Docker containers (no venv)
+
+CORE PRINCIPLES
+Follow DRY principles and write modular, maintainable code
+Include appropriate debug logging and error handling
+ALWAYS ask clarifying questions before making assumptions
+Choose the simplest solution that solves the problem - avoid over-engineering
+Prefer explicit over implicit (Pythonic principle)
+
+CODE STANDARDS
+Python: Follow PEP 8, use type hints, docstrings for functions/classes
+Use structured logging (loguru or structlog) with appropriate levels
+Docker: Multi-stage builds where beneficial, minimize layer count
+Keep docker-compose.yml organized and well-commented
+Environment variables for all config (never hardcode secrets/URLs)
+
+DEVELOPMENT WORKFLOW
+Test changes in Docker container before marking complete
+Include error handling and graceful failure modes
+Update requirements.txt or pyproject.toml when adding dependencies
+After completing each task: create a concise prompt for the next subtask, make a descriptive gitea commit, suggest what to tackle next
+
+DOCKER SPECIFICS
+Always update Dockerfile if dependencies change
+Use .dockerignore to keep images lean
+Verify services can communicate via docker network
+Use health checks in docker-compose where appropriate
+
+AVOID
+Don't create virtual environments (we use Docker)
+Don't over-abstract simple problems
+Don't commit untested code
+Don't push to remote (manual review first)