github-code Web Component

2026-07-07 · Source: Simon Willison's Weblog · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

The github-code Web Component, an experimental tool released on July 7, 2026, was developed using GPT-5.5 and a specific prompt to embed code snippets directly from GitHub. This component processes standard GitHub URLs, converting them into raw content links (e.g., "https://raw.githubusercontent.com/..."). It then utilizes the fetch() API to retrieve the code and displays a user-specified range of lines. Key features include automatic line numbering for the displayed snippets, though it currently does not support syntax highlighting. This component demonstrates a practical application of large language models in generating functional web development tools for content embedding.

Key takeaway

For software engineers or AI engineers exploring rapid prototyping, this component demonstrates how you can utilize advanced LLMs like GPT-5.5 to quickly generate functional web components. If your project requires embedding dynamic code snippets from GitHub, consider using prompt engineering to automate initial component development. This approach can significantly reduce development time for utility features, allowing you to focus on core application logic rather than boilerplate code for content integration.

Key insights

GPT-5.5 can rapidly generate functional web components for specific tasks like embedding GitHub code.

Principles

• LLMs accelerate web component development.
• Prompt engineering guides component functionality.

Method

Prompt an LLM (GPT-5.5) with desired functionality, including URL conversion, data fetching via fetch(), and specific display requirements like line numbering.

In practice

• Embed GitHub code snippets directly.
• Generate web components with LLMs.
• Convert GitHub URLs to raw content.

Topics

• Web Components
• GPT-5.5
• LLM Application Development
• Code Embedding
• GitHub Integration
• Prompt Engineering

Best for: Software Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Simon Willison's Weblog.