The agent successfully assembled a comprehensive overview of simonwillison.net by combining direct fetches of the homepage and about page with web searches and exploration of related projects. The site itself is relatively minimal—a weblog with basic navigation—requiring the agent to piece together the full picture from blog content, external references (Wikipedia, Substack), and prior knowledge of Simon Willison's work. Pricing information was particularly opaque; Datasette Cloud pricing required acknowledging a gap (contact sales model) rather than published rates.

• ›The homepage and /about page were fetched but their HTML was truncated in the responses, forcing the agent to rely heavily on web search results and external sources (Wikipedia, Substack, Datasette.io) to answer 'who is Simon Willison' and 'what does he do'.
• ›The site does not publish pricing for Datasette Cloud directly; the agent had to infer it by visiting datasette.cloud and reporting that pricing is not publicly listed. Newsletter pricing ($10/month on Substack) was not on simonwillison.net itself but found via search and external fetch.
• ›The agent used prior knowledge (40% sourced this way) to understand Simon Willison's background (Django co-creator, open source credibility) and filled gaps with web search (20%) because the site's own content was insufficient for a complete answer.
• ›The site structure is simple (homepage, /about, /tags, year archives) but the actual 'product' explanation required visiting datasette.io, llm.datasette.io, and datasette.cloud—separate properties—and the newsletter required visiting simonw.substack.com. These are not integrated into simonwillison.net itself.
• ›No machine-readable metadata (JSON-LD, structured data, llms.txt equivalents) was evident in the fetched responses; the agent had to read prose and assemble meaning manually or rely on search-surfaced summaries.