A visual AI interface to understand topics/books/papers with LLMs

Hacker News

LLMs make us feel like we can learn anything, but chat is just one primitive. I'm trying to build interfaces which let us cover a topic our sources at length with AI. These interfaces let us consolidate our understanding at glance so we don't get lost in long chat message histories, zoom in and zoom out of information fast and minimize tedious work such as context engineering. Curious if you find this interesting, and have feedback! Please try it (it's free).

Visit product View on Hacker NewsNov 26, 2025

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

91%91% 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.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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