prune, a Chrome extension to help you trim your garden of tabs

Hacker News

Hey friends! Just here to share my open-source Chrome extension that I made awhile ago to help me manage the massive number of tabs I would always have open, hopefully some of you also find it useful. It has a few features that have helped me from having my tab garden grow too out-of-hand: - Focusing any existing tabs instead of opening new ones (regardless of how the link is opened) - Automatically moving tabs to a group called " old tabs" after I haven't looked at them in awhile - Mercilessly removing tabs that I haven't looked at in even longer (if you're a loose cannon you can just enable this) - ...and some other features I made for others but never really use personally. For those using Firefox, sorry, it uses some features of manifest v3 (tab grouping), so it isn't currently available for you. I'm keeping an eye on it and will release something when I can. You can find the source code here: https://github.com/tbrockman/prune Contributions, suggestions, and bug reports are welcome! Hope you find it useful.

Visit product View on Hacker NewsFeb 8, 2022

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

79%79% 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

76%76% 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: exist, existing, io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

12%12% 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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