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Tabius – automatic tab grouping browser extension

Hacker News

Tabius – automatic tab grouping browser extension

Hi HN, I just published version 2 of my tab grouping extension, Tabius. A major redesign - I built it from the ground up consisting of Typescript, Preact, and Vite. More importantly, the project is now open source! Tabius is a browser extension that enhances the tab grouping feature of Chromium based browsers. It is automatic but you can also create custom rules. The extension is packed with useful features like: - Automatically create new tab groups when you open a link in a new tab. - Auto Collapse inactive tab groups. - Limit the browser to only create groups if the new tab matches the original tab's domain. - Create custom tab grouping rules: always create tab groups with a specific name and color from your favorite websites! - Save your favorite tab groups and restore them for later use! - Don't want to create tab groups from a specific site? Create a blacklist from the settings or add the site to the blacklist right from the toolbar! - Option to close an existing tab group when there is only 1 tab left in it. - Limit maximum number of tabs allowed in a group. - Automatically name tab groups based on domain/subdomain or the full URL. - View current tab groups and act upon them by clicking on the extension icon right from the toolbar. - Sync all your settings, tab groups, and custom lists with browser. - ..and many more features. It's a small utility that I use daily and you might find it useful. Any suggestions or pull requests are welcome :)

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos
88%88% 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
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
41%41% 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 · Strong signals: way · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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
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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