Can I vibecode Fabric?

Build a highly functional local-first AI "Second Brain" workspace application, inspired by Fabric.so, designed to seamlessly capture everything, organize nothing, and find anything using AI sensemaking.

Tech Stack: Tauri 2 + React + TypeScript + Tailwind CSS (for modern, premium styling) + SQLite (for local storage) + an LLM integration (e.g., OpenAI API or local Ollama).

Core Philosophy: The user should never have to manually categorize, tag, or folder their saved items. The AI should handle reasoning, connecting, and retrieving based on natural language queries, turning a cluttered workspace into a unified memory layer.

Key Features to Implement:

1. Universal Quick Capture & Ingestion:
- Implement a global keyboard shortcut that summons a minimal, Spotlight-like quick-capture palette anywhere on the OS.
- Support capturing diverse data types: text snippets, URLs, markdown notes, and drag-and-drop images.
- Automatically extract rich metadata (title, summary, source URL, date) immediately upon capture using lightweight processing.

2. AI Sensemaking & Semantic Connections:
- Integrate an LLM (configured via API key in settings) to deeply process every captured item in the background.
- The AI must generate concise summaries, extract key concepts/entities, and automatically create semantic "links" to other related items in the database.
- Visual Organization: Provide a clean visual interface that automatically surfaces related ideas and documents without manual folder management, perhaps using a dynamic graph or clustered masonry layout.

3. Natural Language Search Engine:
- Discard traditional exact-match keyword search in favor of semantic, natural language retrieval.
- Implement a conversational search bar where users can ask complex questions like, "Show me the PDF about user retention I saved last month" or "What were those design inspiration images?".
- Use embeddings (stored locally in SQLite using vector extensions, or simulated via LLM filtering for MVP) to retrieve highly relevant items and synthesize a direct answer or display the exact artifacts.

4. UI/UX Design Aesthetics (CRITICAL):
- Implement a stunning, premium design that wows the user immediately. Avoid generic, plain aesthetics at all costs.
- Use a sleek dark mode by default with a curated, harmonious color palette (e.g., deep grays with vibrant, dynamic accent colors like neon purple, electric blue, or subtle glassmorphism effects).
- Use modern typography (e.g., Inter, Roboto, or Outfit) to elevate the feel of the application.
- Incorporate smooth micro-animations for all interactions (hover states, opening notes, search transitions, AI "thinking" states) to make the app feel alive and responsive.
- A fluid, masonry grid layout for the main "timeline" or "canvas" view of all saved artifacts.

5. Local-First & Privacy:
- Emphasize user ownership: All data, including the vector index and raw files, must be stored purely locally using SQLite and the local filesystem via Tauri APIs.
- Provide a robust export-to-Markdown functionality so data is never locked into the platform.

Development Requirements:
- Start from an empty folder and create the complete working project.
- Provide a robust .env.example for any required API keys (e.g., OpenAI/Anthropic for sensemaking/search).
- Write focused unit tests for the capture pipeline, the data model, and the search retrieval logic.
- Ensure accessible keyboard navigation throughout the app, with sensible contrast and focus states.
- Include clear empty, loading, validation, success, and failure states to ensure a polished user experience.
- 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.