Show HN: AI-First Web – SEO for AI Assistants
More and more users don’t browse — they ask AI assistants. But most websites are still built only for human eyes, not for models that need structure, semantics, and metadata to understand content reliably.
AI-First Web explores what “SEO for AI” looks like: how to structure a website so that AI assistants can actually read it, interpret it, and cite it correctly. A well-structured, semantic, machine-readable site increases the chance that your page becomes the cited source for an AI-generated answer.
The project is very early and still evolving — I’d love feedback from people experimenting with how LLMs parse HTML, JSON-LD, and web structure in the real world.
A small additional note for context:
I’m not arguing that “LLMs will replace browsing” in some absolute way — but it is observable that for many users, the entry point for information is shifting from search → assistant. When you actually inspect how models consume real websites today, the results are pretty uneven:
pages with clean HTML and predictable structure get parsed reliably
JSON-LD is used surprisingly often (but only if it’s correct and minimal)
heavy client-side rendering breaks extraction more than people expect
semantic markup still beats any “AI-enabled” tool by a mile
models hallucinate less when the source has clear hierarchy and meaning
This project isn’t trying to reinvent SEO — it’s more like exploring the minimum structural guarantees that make an LLM treat a page as a trustworthy, cite-able source instead of ignoring it or misreading it.
If anyone here has done experiments with:
how GPT, Claude, Gemini, Llama, etc. read arbitrary web pages
failure cases in parsing / hallucination caused by layout
the effect of metadata vs full-text signal
or even prompt strategies for web ingestion
…I’d genuinely love to compare notes.
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