Astro powers this site, but the real unlock is the content collection sitting behind it. Each notebook I publish begins as a Python pipeline that aggregates USPTO bulk data, chemical literature, and my own lab notes. I normalize the output into Markdown + frontmatter so that Astro can type-check every entry before it ships.

Key ingredients:

• Collections + schemas. defineCollection throws an error when a post is missing metadata like filing date, art unit, or technology tags. That friction is welcome—it mirrors the discipline of a patent disclosure form.
• Programmatic ingestion. A short script converts CSV exports and JSON from the PatentsView API into Markdown drafts. I keep IDs human-readable so URLs stay stable when I move content around.
• AI-assisted summaries. LLMs provide first-pass highlights of independent claims or chemical scaffolds. I keep prompts + outputs in the same doc so reviewers can audit the reasoning.

This workflow scales down to a single invention disclosure and up to thousands of families. The code is simple (mostly Pandas + Jinja), but the consistency lets collaborators scan analyses without hunting through spreadsheets.