Paul Graham Essay Summarizer

Target Audience

• Content Creators: Individuals who need to gather and summarize essays for blogs or articles.
• Students: Those looking for resources and summaries of essays for research or study purposes.
• Educators: Teachers who want to provide students with curated content and insights from various essays.
• Researchers: People conducting studies that require literature reviews or synthesis of essay content.
• Developers: Those interested in automating content gathering and summarization processes using n8n.

Problem Solved

This workflow automates the process of fetching, extracting, and summarizing essays from the Paul Graham website. It addresses the challenge of manually collecting and synthesizing information from multiple essays, saving users time and effort. By using this workflow, users can quickly obtain summaries of the latest essays without needing to read through each one individually.

Workflow Steps

1. Manual Trigger: The workflow begins when the user clicks the "Execute Workflow" button.
2. Fetch Essay List: It makes an HTTP request to retrieve a list of essays from the specified URL (http://www.paulgraham.com/articles.html).
3. Extract Essay Names: The workflow extracts essay links using HTML parsing, targeting specific elements in the page structure.
4. Split Out Into Items: The extracted essay links are split into individual items for further processing.
5. Limit to First 3: It limits the processing to the first 3 essays to manage content volume.
6. Fetch Essay Texts: For each of the selected essays, it makes another HTTP request to fetch the full text.
7. Extract Title: The title of each essay is extracted from the HTML content.
8. Clean Up: The relevant data (title, summary, URL) is cleaned and structured for output.
9. Sticky Notes: Two sticky notes are created to display information about the workflow and summarization steps.
10. Summarization Chain: The essay texts are processed using LangChain to generate summaries.
11. OpenAI Chat Model: The summaries are enhanced using OpenAI's language model.
12. Merge: The final structured data is combined for output, ready for user review.