Article Summary

Accepts article URLs as input, performs server-side content extraction (likely using a headless browser or DOM parser to isolate article text from boilerplate), and pipes the extracted text through an LLM API (OpenAI, Anthropic, or similar) to generate a concise summary. The Vercel edge deployment enables sub-second latency by executing extraction and API calls close to the user's geographic region.

Generates summaries using a fixed, non-configurable compression ratio (likely 30-50% of original text length) via prompt engineering or model-specific parameters sent to the LLM. The approach prioritizes consistency and predictability over user control—all summaries follow the same brevity standard regardless of source article length or user preference.

Implements a synchronous, request-response architecture where each summarization request is independent—no session state, no request queuing, no result caching. The Vercel serverless function receives a URL or text, executes extraction and LLM inference in a single HTTP call, and returns the summary immediately. No database or persistent storage is involved, keeping infrastructure minimal and costs proportional to usage.

Provides a minimal, single-page web interface (likely React or vanilla JS on Vercel) with a text input field for URLs and a submit button. The UI handles client-side form validation (checking for valid HTTP/HTTPS URLs), sends the URL to the backend via fetch/axios, and displays the summary in a read-only text area. No authentication, no navigation menus, no distracting sidebars—the entire app is one focused interaction.

The backend abstracts the LLM provider behind a configuration layer, allowing the operator to swap between OpenAI, Anthropic, or other API providers by changing environment variables. The summarization logic sends a standardized prompt template to the selected LLM, handling provider-specific differences in API format, authentication, and response parsing. This architecture enables cost optimization (e.g., switching to cheaper models) and model upgrades without code changes.