PDF reader with interactive visualizations for research papers

Hacker News

Hey HN, I built a PDF reader that can generate interactive visualizations right inside the document. The goal is to reduce the “intuition gap” when reading dense papers: select a concept, click Visualize, and it tries to produce a solid visual interactive app where you can rotate/zoom/step through (not just a text explanation). Upload any PDF, select text, click Visualize. Demo (no signup): https://zerodistract.com/try/pdf/67cdee74-810b-4f1b-af7d-010... Product link: https://zerodistract.com I’d love feedback, especially on what feels useful vs. distracting, and where it breaks.

Visit product View on Hacker NewsFeb 27, 2026

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8points

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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

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

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: visual · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

35%35% 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

15%15% 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

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

Correct prediction on native model

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