A Chrome extension for Delicious/Pinboard

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My partner and I are avid users of Delicious/Pinboard and wanted a quick way to add to and access our stored bookmarks. We created a Chrome extension to scratch that itch. It shows your ten most-recent bookmarks by default, with a fast search interface to find older bookmarks, and keyboard shortcuts for navigation. We plan to add pagination in an upcoming release, and have considered adding support for Safari as well as alternate bookmarking backends. The (mostly CoffeeScript) source is up on GitHub, and may be interesting as an example of caching Backbone Collections in localStorage or using in-page iframes to simulate opening a Chrome extension popup via keyboard shortcut. Is this something you find useful? We welcome comments or suggestions. https://chrome.google.com/webstore/detail/ndjggnnohdkheiijjhbklkanjcpibbng https://github.com/cabin/linkhunter

Visit product View on Hacker NewsMar 5, 2012

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, using · Missing: mac, agents, macos

68%68% 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.

AppSumoStrong fit for a featured deal · Strong signals: interface, users · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: google, users, way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

43%43% 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

28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% 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.

Incorrect prediction on native model

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