Collate – Offline AI PDF reader and chat for Mac (private, on-device)

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I built Collate, a free Mac app that summarizes and chats with PDFs entirely on your device. No uploads, no rate limits, just open a file and ask questions. It highlights the exact passages it used and shows citations, so you can verify answers quickly. Why: I wanted AI that feels like a normal app—fast, private, and not tethered to a cloud account—especially for long papers and reports. How it works (high level): • Extracts text with native macOS frameworks (works best with text-based PDFs). • Chunks + indexes the doc locally for retrieval. • Runs a small LLM on Apple Silicon using on-device inference. • Maps answers back to page spans and auto-highlights them. What’s new: share clean summary links (text only, optional), faster summaries, and a progress tracker that shows how much of the doc you’ve covered as you chat. Known limits: macOS 13.5+ on Apple Silicon; large/scanned PDFs (pure images) work poorly without OCR; still polishing multi-PDF answers and edge cases. Why: If AI is going to be an equalizer, it shouldn’t meter every question or upload private files. Collate runs on your device and removes the limits. What features would be the biggest unlock for you? Links: https://collate.one Happy to answer technical questions.

Visit product View on Hacker NewsAug 21, 2025

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

90%90% 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

84%84% 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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% 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

17%17% 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

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

Incorrect prediction on native model

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