Interactive Literature Reviews with Visual Knowledge Maps

Hacker News

I built a tool for academics and practitioners that turns literature reviews into interactive, visual experiences. Upload PDFs and HTML sources, and AI creates a knowledge map showing how everything connects. What it does: - Upload research papers (PDFs) and web sources (HTML) - AI generates interactive knowledge maps from your sources - Skim read at a high level or read per-source/section summaries - Expand/build further specific sections of the map that interest you - Read original sources in context - Ask questions about the research and get contextual answers I have curated some reviews on LLMs, Diffusion Models, Vision Language Models, AI Agents, Robotics, and Text-to-Speech. Why I built this: I was constructing lit reviews with ChatGPT and getting lost in the walls of chat; I wanted to make a better experience for myself. Please give me feedback on the UX/anything else!

Visit product View on Hacker NewsJun 18, 2025

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

97%97% 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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: reviews · Missing: plus, platform, intuitive

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

TrustMRRLess likely to generate early MRR · Strong signals: answers · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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