AI Native Reading Companion

Hacker News

What would reading look like if it were designed around how people actually learn? I got tired of all the AI study tools that actually just take the cognitive work of studying or reading away from you, instead of building up your own brain. Reading today is also just not the best experience as you forget so much of what you read. I built Mimir, (readmimir.com), an interactive immersive reader that let's you upload any book/text, read in immersive reading with TTS, active recall, in-margin notes, ask-ai tutor, and automatically creates spaced repetition notes from your notes and answers. Mimir is currently free, I would love your feedback!! (more importantly, we're designing some social features - like sharing your margin notes with friends or followers, allowing "community notes" to posted essays, doing reading groups with friends etc!, and if you have ideas for that, i'd love to hear!!)

Visit product View on Hacker NewsJan 26, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: notes · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: answers, way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: margin, active · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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