Can I vibecode Litmaps?

Build the closest honest consolation tool inspired by Litmaps; do not claim to replace its structural moat.
Use exactly this stack: Next.js 15 + TypeScript + SQLite + Playwright.
Primary job: Build a private literature discovery workspace that imports user-supplied URLs or files, extracts metadata, supports notes, and searches the local corpus.
Start from an empty folder and create the complete working project.

Functional Requirements:
- Data Ingestion: Use Playwright to automatically navigate to user-provided scholarly URLs, bypassing simple banners, and extracting the abstract, title, and authors.
- Literature Mapping: Create a basic visual node-graph (using a library like React Flow) to map out how imported papers relate based on shared keywords or manual user-defined links.
- Note-Taking: Provide a simple markdown editor attached to each paper node.
- Local Search: Implement an indexed local search to find papers by title, author, or keyword within the SQLite database.
- PDF Handling: Allow local PDF uploads and extract raw text using a lightweight local parser.

Architecture & State:
- Make the default mode single-user and private.
- Store user data, extracted text, and the network graph locally in SQLite.
- Do not add analytics, telemetry, ads, or third-party accounts.
- Put every secret and external credential in .env and provide .env.example.

UI/UX & States:
- Implement the smallest polished interface that completes the core loop end to end, focusing on the visual graph interface.
- Include clear empty, loading (during Playwright scraping), validation, success, and failure states.
- Add export functionality to save the bibliography as standard BibTeX or CSV.
- Use accessible keyboard navigation, labels, focus states, and sensible contrast.

Security & Constraints:
- Validate untrusted input and never log secrets or private file contents.
- Deliberately exclude these paid-product advantages: massive global citation graph scale; team libraries and institutional access; licensed scholarly metadata feeds.
- Do not fake integrations, network effects, proprietary data, model quality, compliance, or security claims.

Testing & DevOps:
- Write focused unit tests for the data model, graph traversal, and metadata extraction.
- Add one end-to-end smoke test that proves the core loop works.
- Create a README with setup, permissions, architecture, data location, backup, and limitations.
- Add scripts for install, development, test, build, and a production-style local run.
- Run the tests and build before finishing, then fix errors rather than merely describing them.