ReadAgain

Status

Live

Platform

Web (Next.js/Supabase)

Type

Lean Product

Focus

AI-generated book recommendations grounded in the reader's own emotional memory

There's a large set of people who've checked out of reading — never quite connected to it, increasingly pulled away by their phones, or just haven't found the right book to pull them back in. But almost everyone, at some point, has had a moment where a book truly connected with them. Other tools in this space optimize for genre and popularity — not for what actually makes a reader feel connected, and not for who they really are.

ReadAgain starts by asking readers to return to a book (or a few) they remember truly connecting with, and to recall why. From that, it builds a first Reader DNA profile and a starting set of five recommendations — each one explained by tracing it back to the reader's own memories. From there, the reader begins a journey back into reading: discovering new books aligned with what they actually connect with, and learning more about themselves as a reader as their DNA keeps evolving.

• Decision 1

  Guided, emotionally specific onboarding over multiple-choice.

  Shallow inputs produce shallow profiles. The onboarding asks reflective questions instead of checkbox categories, because the entire product depends on DNA quality.

• Decision 2

  Explain through memory, not metadata.

  Recommendations are framed around the reader's own past reading experiences, not genre tags or "similar users." This is the actual differentiation — anyone can match genres.

• Decision 3

  An evolving profile, not a one-time quiz.

  Reader DNA isn't fixed at onboarding — it develops as the reader engages with more recommendations, so the product deepens its understanding of them over time instead of treating them as a single static input.

Live at readagain.app, launched publicly. Readers are building their Reader DNA profiles and receiving recommendations grounded in their own memories of the books that mattered to them.