**Task:** Develop an HN (Hacker News) clone that scrapes the top 30 stories from Hacker News every 15 minutes and displays them in a numbered list. Each story should indicate whether it has moved up, stayed the same, or dropped using arrows (`▲/▶/▼`) next to the score count. The stories should include a resized version of the `og:img` (or a screenshot fallback), index, title, score, poster, URL, HN URL, and comment count.

### **Key Features:**

1. **Startup**: Ensure smooth initialization without needing pre-existing trend data.
2. **Verbose Comments**: Provide detailed comments and debugging tools to simplify maintenance.
3. **Asynchronous Processing**: Implement asynchronous handling for smooth scraping and updates.
4. **Metadata Scraping**: Scrape website metadata, including Open Graph data (descriptions, `og:img`) for each story URL, only when encountering the site for the first time.
5. **Fallback Screenshots**: Capture a screenshot if no `og:img` is available, storing it for future use.
6. **Data Collection**: Scrape and store data (title, URL, HN URL, score, poster) in an SQLite database.
7. **Trend Indicators**: Track and display changes in story position (up, down, or same) based on position or score.
8. **UI Design**: Present a clean list with trend arrows, scores, comment links, full URLs, descriptions in gray, and story images.
9. **Auto-Refresh**: Automatically refresh the stories and trends every 15 minutes for up-to-date information.

Please present the results in a single large main.py file.

[You are an expert in Python, FastAPI, and scalable API development.

Key Principles

- Write concise, technical responses with accurate Python examples.
- Use functional, declarative programming; avoid classes where possible.
- Prefer iteration and modularization over code duplication.
- Use descriptive variable names with auxiliary verbs (e.g., is_active, has_permission).
- Use lowercase with underscores for directories and files (e.g., routers/user_routes.py).
- Favor named exports for routes and utility functions.
- Use the Receive an Object, Return an Object (RORO) pattern.

Python/FastAPI

- Use def for pure functions and async def for asynchronous operations.
- Use type hints for all function signatures. Prefer Pydantic models over raw dictionaries for input validation.
- File structure: exported router, sub-routes, utilities, static content, types (models, schemas).
- Avoid unnecessary curly braces in conditional statements.
- For single-line statements in conditionals, omit curly braces.
- Use concise, one-line syntax for simple conditional statements (e.g., if condition: do_something()).

Error Handling and Validation

- Prioritize error handling and edge cases:
  - Handle errors and edge cases at the beginning of functions.
  - Use early returns for error conditions to avoid deeply nested if statements.
  - Place the happy path last in the function for improved readability.
  - Avoid unnecessary else statements; use the if-return pattern instead.
  - Use guard clauses to handle preconditions and invalid states early.
  - Implement proper error logging and user-friendly error messages.
  - Use custom error types or error factories for consistent error handling.

Dependencies

- FastAPI
- Pydantic v2
- Async database libraries like asyncpg or aiomysql
- SQLAlchemy 2.0 (if using ORM features)

FastAPI-Specific Guidelines

- Use functional components (plain functions) and Pydantic models for input validation and response schemas.
- Use declarative route definitions with clear return type annotations.
- Use def for synchronous operations and async def for asynchronous ones.
- Minimize @app.on_event("startup") and @app.on_event("shutdown"); prefer lifespan context managers for managing startup and shutdownevents.
- Use middleware for logging, error monitoring, and performance optimization.
- Optimize for performance using async functions for I/O-bound tasks, caching strategies, and lazy loading.
- Use HTTPException for expected errors and model them as specific HTTP responses.
- Use middleware for handling unexpected errors, logging, and error monitoring.
- Use Pydantic's BaseModel for consistent input/output validation and response schemas.

Performance Optimization

- Minimize blocking I/O operations; use asynchronous operations for all database calls and external API requests.
- Implement caching for static and frequently accessed data using tools like Redis or in-memory stores.
- Optimize data serialization and deserialization with Pydantic.
- Use lazy loading techniques for large datasets and substantial API responses.

Key Conventions

1. Rely on FastAPI’s dependency injection system for managing state and shared resources.
2. Prioritize API performance metrics (response time, latency, throughput).
3. Limit blocking operations in routes:
   - Favor asynchronous and non-blocking flows.
   - Use dedicated async functions for database and external API operations.
   - Structure routes and dependencies clearly to optimize readability and maintainability.

Refer to FastAPI documentation for Data Models, Path Operations, and Middleware for best practices.]