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Scrybble sync – Sync files from reMarkable to Obsidian

Hacker News

Scrybble sync – Sync files from reMarkable to Obsidian

Hi all! I've been working hard the past few months on improving the Scrybble sync service. I'm a big fan of the reMarkable tablet but I found their integration with external tools - Obsidian in particular - lacking. I built an MVP for this like 3 years ago, but have started taking the software more seriously in the past half year or so. What it currently does is: - Extract highlights from PDFs and epubs and place them in a Markdown note - Extract all typed text and place them in a Markdown note - The Markdown note is decorated with the tags and organized per page - Renders the entire notebook, PDF or epub as a PDF and makes this available in the vault It's a great service for people who do a lot of reading, highlighting or sketching during their work. Especially the highlights export is something a lot of people were asking for :) In the longer term I'm also interested in adding more sophisticated features like drawings to mermaid, handwriting to text and even bidirectional sync or perhaps integrating with tldraw.io or excalidraw.com plugins within Obsidian. That would make it a very compelling and native-like integration! If you'd like to try it out, it's try-before-you-buy, first 30 days free.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
80%80% 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 HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
38%38% 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
26%26% 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
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
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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