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Petrarca: Voice first spaced repetition – track knowledge across books

Petrarca: Voice first spaced repetition – track knowledge across books

by houshuang·Apr 7, 2026·1 point·0 comments

AI Analysis

●●SolidNiche GemCozy

Voice-first knowledge elicitation beats typing notes, but Readwise already does resurfacing.

Strengths
  • Voice prompts force prioritization differently than written note-taking
  • Six years of iteration shows genuine problem-solution fit
  • Contextual resurfacing ties reviews to what you're currently reading
Weaknesses
  • Crowded category with Readwise, Obsidian, Logseq, and Anki
  • Unclear how this differs from existing spaced repetition apps
Category
Target Audience

Self-directed learners, adult readers of non-fiction

Similar To

Readwise · Obsidian · Anki

Post Description

I've been struggling for years to get an overview (become literate) in history as an adult. I wanted all the names (Cicero, Caesar, Constantinople, Waterloo) to actually mean something, because reading books and deeper analyses becomes so much more interesting then.

But I also didn't want an app that forced me to do all my reading on screen - I want to read physical books on the couch, listen to a history podcast when I walk, watch a documentary with my wife. And still track what I learnt, and maintain it...

So I built an app that let's me track which books I am reading by taking a picture of the cover (it automatically researches them, inserts TOC and content, autosyncs with Kindle progress etc), and generates review questions based on book content and generated "curricula".

But to map the things I already know, I designed a "knowledge elicitation" using voice - it gives me some prompts (what do you know about Plato), I talk into the mic while walking, and it maps what I know, my mistakes, my gaps, and also what I'm curious about. What I'm curious about (how was Plato different from Socrates?) becomes a micro-learning card that is scheduled together with the review cards...

So far it's proving really valuable. Lot's to tune as I keep using it, but I feel the combination of deep learning offline, and review digitally when you have a few minutes free, is the best of both worlds.

Repo here: https://github.com/houshuang/petrarca (point Claude Code at it, requires a VM if you want it on your phone, some API keys etc)

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