Parsing Webpages with a LLM – Revisited (hdembinski.github.io)

This revisited example shows a practical pipeline for converting live, JavaScript-rendered webpages into Markdown for downstream LLM use. The script uses Playwright (sync API) to launch a headless Chromium, navigates to pages and waits for network-idle to capture rendered HTML, then passes the content through markdownify to produce Markdown files whose names are derived from the URL. Execution is parallelized with joblib.Parallel (n_jobs=4) and skips files that already exist — the run shown returned "Skipped ..." for five saved filenames, indicating those pages had been previously scraped. For the AI/ML community this pattern is significant because it demonstrates how to build cleaner, LLM-consumable corpora and retrieval indices from modern web apps that require JS rendering. Key technical takeaways: use of headless browsers to capture client-side content, converting HTML→Markdown for text normalization, simple URL→filename sanitization, and parallel execution to scale throughput. Also note practical caveats: markdownify can lose semantic structure, headless browsers are resource- and rate-limit-sensitive, and robust error handling, politeness (rate limits/robots.txt), deduplication, and provenance metadata are essential when preparing datasets for fine-tuning or retrieval-augmented generation.