---
title: Library
canonical_url: https://tkmstudio.com/library
language: en
last_updated: 2026-07-13
organization: TKM Studio, AI Visibility & Adoption Studio, Madrid, working worldwide
---

# Library: how AI visibility actually works

Reference articles on Answer Share, machine readable infrastructure and how AI engines select what they cite. Written from methodology we run in production, on our own brands and on client builds.

## Articles

- [What is Answer Share?](https://tkmstudio.com/library/what-is-answer-share) -> https://tkmstudio.com/md/en/what-is-answer-share.md
  The visibility metric of the AI answer era: definition, measurement structure, the open-intent split and what moves the score.
- [Content negotiation for AI agents: serving markdown with the Accept header](https://tkmstudio.com/library/content-negotiation-for-ai-agents) -> https://tkmstudio.com/md/en/content-negotiation-for-ai-agents.md
  How the same URL serves a clean markdown rendition when an agent asks, why user agent triggering breaks extraction, and why prerendered HTML stays the default.
- [Why AI engines cite platforms, not websites](https://tkmstudio.com/library/ai-engines-cite-platforms-not-websites) -> https://tkmstudio.com/md/en/ai-engines-cite-platforms-not-websites.md
  Engines justify recommendations with review platforms, directories, press and community. What the citation source pattern means for a visibility roadmap.

More articles are in preparation and will be published here.

Contact: https://tkmstudio.com/contact