I am building a Safari Web Extension to switch tabs efficiently

Hacker News

I like the tab-switching behaviour in Chrome/Firefox and wanted Safari to have something similar. Another wish of mine was for it to be a web extension that feels native to the browser vs an app extension that looks out of place. After waiting long enough, I decided to build one myself. A little preview of Tap Tab - https://taptab.manibatra.xyz . The web extension APIs for managing tabs are pretty locked down in Safari, but I have already found a lot of value in using the extension. Some of the features that I find very useful(in the middle of building a few of these) : - Driven by keyboard shortcuts. I can see the tabs I have open at a glance and switch between them. - Fuzzy search to quickly filter down to the tab you want. - Quick switching between two tabs. - Ability to pin, mute, and close the tab right from the extension. Say hello, and tell me what features you would like the extension to have!! You can follow the updates on Twitter- https://twitter.com/ManiBatra23 .

Visit product View on Hacker NewsDec 1, 2022

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

87%87% 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 · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting

73%73% 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

50%50% 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 · Strong signals: efficient · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

39%39% 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

14%14% 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.

Incorrect prediction on native model

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