The agent successfully pieced together a comprehensive explanation of vgpu by combining documentation fetched directly from the site (vgpu.sh/docs structure) with the GitHub README. The site's documentation is well-structured and machine-readable (.md files), but the agent had to work around a fragmented identity—the main site (vgpu.sh) lacks marketing copy, pricing clarity, and comparison sections, forcing heavy reliance on the README and prior knowledge to construct the full narrative.

• ›Step [13] (vgpu.sh/docs.md) and [38] (vgpu.sh/docs/get-started.md) returned clean markdown documentation describing the core value prop (WebGPU library with agent-first design, multi-runtime support), but did not explain pricing, alternatives, or differentiation.
• ›Step [40] (concepts.md) and [41] (agents.md) were content-bearing and clearly explained the agent-first positioning and core API concepts, confirming vgpu's niche within the documentation itself.
• ›Step [50] (GitHub README) was the primary source for pricing (MIT license, free/open-source), bundle size claims (25 KB vs 500 KB engines), and technical differentiation from Three.js/Babylon.js—information NOT present on vgpu.sh itself.
• ›The site lacks standard navigation pages (vgpu.sh/pricing → 404, vgpu.sh/about → 404), forcing the agent to infer free/open-source status from prior knowledge and GitHub discovery rather than finding it on the primary domain.
• ›The agent discovered the GitHub repository through web search (steps [28], [46]), not through site navigation or links—vgpu.sh does not prominently surface its GitHub URL or README in fetched HTML.
• ›The site's agent-readiness is mixed: documentation is well-organized in markdown format and the 'agent-first' positioning is stated, but the marketing narrative, pricing, and comparison content lives in README.md on GitHub, not on the primary domain.