Reater

Methodology

Reater answers one question: what do influential people actually read? Not what their publicist says they read. What they name, unprompted or on the record, in public.

What counts as a recommendation

Why sources are the product

Tracking where a recommendation surfaced turns a list into a dataset. It shows which podcasts surface the most books, which newsletters break new picks first, and how taste travels between fields. The charts page is built entirely from this source metadata.

Podcasts

A host asks what a guest is reading, or a guest brings it up unprompted.

YouTube

Interviews, lectures, and long-form conversations.

Blogs

Personal essays and annual lists published on personal sites.

Interviews

Print and broadcast interviews with a reading question.

Newsletters

What authors put in their own dispatches.

Books

Books recommended inside other books: forewords, bibliographies, acknowledgments.

Posts

Short-form public posts naming a book.

Reading lists

Official annual or topical lists published by the figure.

Built for AI agents

Reater is agent-first. Every page is semantic HTML with JSON-LD, and the whole dataset ships as JSON:

Money and independence

Reater earns as an Amazon Associate when readers buy through links on this site, and through Audible referrals. Rankings are never for sale: order is computed from the data, and sponsored placement does not exist here.

Corrections welcome. If a recommendation is misattributed or a source link rots, it gets fixed or removed.

Image credits

Figure portraits come from Wikimedia Commons. Public-domain images need no credit; the rest are used under their Creative Commons licenses, credited here:

Portraits of Barack Obama, Oprah Winfrey, Warren Buffett, and Jensen Huang are public-domain official photographs. Book covers are served from the Open Library Covers API.