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Snipscribe – a digital organizer for your pen and paper notes

Hacker News

Snipscribe – a digital organizer for your pen and paper notes

Hi HN, I’m building Snipscribe, a desktop application (beta - demo version) for people who still prefer thinking on paper but want the benefits of digital organization. The idea is simple: You take a photo of your handwritten notes, split them into small snippets, and tag them so you can retrieve them later by topic instead of scrolling through folders of images. I've found pen and paper to be the most intuitive, distraction-free way to capture ideas, but the hardest part is finding them again and organizing for easy access. Snipscribe focuses on solving just that problem—without trying to replace your notebook. What it does: 1. Capture photos of handwritten notes 2. Break them into small, focused snippets using a notation in your notes 3. Tag snippets for quick retrieval later This is still early, and I’m especially interested in feedback from: - People who regularly use notebooks or index cards - Developers, designers, or students who mix analog + digital workflows - Anyone who’s tried (and bounced off) digital note apps Website: https://snipscribe.com Happy to answer questions, and I’d really appreciate any candid feedback. Please let me know what feels useful, what’s missing, or what you’d simplify. Thanks for reading :)

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Actual performance

3points
2comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, using, notes · Missing: mac, agents, macos
81%81% 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
58%58% predicted probability of success on Indie Hackers, 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
44%44% 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: apps, way · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive · Missing: plus, platform, reviews
37%37% 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
14%14% 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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