Tabliner, a fast keyboard-driven tab manager for Chrome

Hacker News

Chrome Web Store: https://chrome.google.com/webstore/detail/tabliner/aglmjcobpaikhghnfnioeipbddcgpmad?hl=en GitHub: https://github.com/uyhcire/tabliner For a long time, I've struggled with having too many tabs open. I tried looking through my tabs each week, and using tab manager Chrome extensions, but I still didn't have a good way to quickly look over and close my tabs. With all the other extensions I tried, I felt like there was too much friction when I tried to close tabs. So I wrote an extension for myself, focusing on making it really easy to open, use, and go back. The key for me has been to make all actions keyboard-driven: you can do anything you need without leaving your keyboard. I've been using it myself for a few months and I find it far faster and easier to use than anything I've tried before. I hope you all find it as useful as I did! A quick tutorial: - From anywhere, jump to the list of tabs with Ctrl+. or Cmd+. - Use the arrow keys to navigate - Press Backspace to close tabs - Then jump back to your original tab by selecting it and pressing Enter

Visit product View on Hacker NewsOct 5, 2019

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

86%86% 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

71%71% 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, google, way · Missing: mobile apps, ios, personal

47%47% 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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

36%36% 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 · Strong signals: arr · Missing: mrr, revenue, profit

13%13% 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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