Bookmarking and note taking web-app focused on keyboard shortcuts

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Hi all, I wanted your opinion on this quick project I threw together over the past couple of days. It's the typical bookmarking and note-taking web-app, but with a strong focus on keyboard shortcuts, since I love them in Gmail, Reddit (with the Reddit Enhancement Suite), etc. It also syncs with your Dropbox account if you want it to, so you can keep your bookmarks and notes with you on all your devices and modify them wherever you are. You can also import bookmarks from Delicious. More importing options coming soon. There's public and private RSS feeds for your notes and bookmarks, an easy to use API which probably needs work and isn't very secure at the moment, bookmarklets, and a very simple HTML scraper so you can even view a bookmark's content right within the app, and much more! http://jabjot.com/ List of shortcuts: http://jabjot.com/tools#tools_shortcuts Tools: http://jabjot.com/tools PS: I know the design is godawful, will revisit it soon! PPS: Gonna probably open source the code in the following days.

Visit product View on Hacker NewsMar 5, 2012

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2points

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Did not reach leaderboard

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Product HuntOn track for Day 1 leaderboard · Strong signals: notes, code, open · Missing: mac, agents, macos

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

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

66%66% 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 · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

18%18% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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