MetaShelf — Bibliographic Resource Discovery Platform

Delivered a working MVP: resource list and create APIs, Pydantic validation, automatic SQLite table creation with a seed script, and a catalog frontend featuring a search-bar placeholder and a 'New Arrivals' section. It ships with a clean layered architecture and a clear roadmap toward PostgreSQL, many-to-many subject relations, and JWT auth.

• Five-layer Clean Architecture (Models/Schemas/Repositories/Services/API) with clear separation of concerns and easy extensibility
• FRBR-lite data model separating bibliographic Resource from physical Item, preserving LIS metadata (ISBN, call number, subject headings)
• Fully async stack: FastAPI + SQLAlchemy 2.0 async + aiosqlite for end-to-end async I/O
• Modern frontend: React 19 + Vite + Tailwind CSS v4 + TanStack Query with a responsive, dark-mode card UI
• Repository pattern encapsulating data access, paired with Pydantic v2 DTOs for request/response validation
• Solid DX: seed script, Vite proxy, and a one-command start_dev.sh launching both backend and frontend

• Balancing LIS rigor against MVP velocity — using FRBR-lite and comma-separated subject headings as deliberate simplifications for fast iteration
• Managing async SQLAlchemy 2.0 engine/session lifecycles and startup-time table creation
• Designing for smooth upgrades (SQLite->PostgreSQL, string author->relational Agent authority control)

• Portfolio reviewers and interviewers
• Technical readers who need a quick view of purpose, stack, and maturity

• Detected technical signals: Python, FastAPI, SQLAlchemy 2.0 (Async), Pydantic v2, SQLite, aiosqlite, Alembic, Uvicorn, React 19, TypeScript, Vite, Tailwind CSS v4, TanStack Query, Axios, Zustand
• README evidence exists and can support a fuller reviewed case study
• A public GitHub repository is not verified yet; the portfolio marks it as pending

This case study is generated from the portfolio catalog pipeline using README, Git metadata, package/build configuration, and media signals. The final architecture narrative still needs source-level review. Current detected technology signals include: Python, FastAPI, SQLAlchemy 2.0 (Async), Pydantic v2, SQLite, aiosqlite, Alembic, Uvicorn, React 19, TypeScript, Vite, Tailwind CSS v4, TanStack Query, Axios, Zustand.

A public data-flow narrative is not fully reviewed yet. If the project includes data processing, AI pipelines, or backend APIs, the next pass should document input, processing, storage, and UI/output flow end to end.

metashelf/
  README.md              # project documentation, when available
  source files           # implementation reviewed by local audit
  package/build config   # detected capability signals

This project does not expose a verified runnable web command yet. Review the README/source tree and add exact install, run, test, and build commands before interview use.
No verified build command was detected. Treat the current portfolio page as a case-study placeholder until build steps are reviewed.

• Complete the production-quality README, screenshots, and demo recording
• Add architecture diagrams, data-flow notes, and key technical decisions
• Verify build/test status and update the portfolio release report

• State the current maturity and demonstrable scope first
• Focus on verified stack, source structure, and completed behavior
• Do not claim unverified deployment, video, or test coverage as finished

• Implement Item inventory management and auth endpoints (OAuth2 Password Flow + JWT)
• Promote subject headings and authors to many-to-many tables with Agent authority control, and adopt Alembic migrations
• Round out the frontend: wire up real search, a resource detail page, My Lists, and pagination