I3wm Alt+Tab Workspace Switcher

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https://github.com/iondodon/i3-switcher-x11 The i3wm Alt+Tab Workspace Switcher is a tool designed to bring the familiar Alt+Tab window switching functionality to the i3 window manager environment. It aims to enhance productivity by enabling users to switch between workspaces efficiently and intuitively, mimicking the window switching feature found in traditional desktop environments like Windows. In my experience, using the $mod+workspace number to switch workspaces feels slower. I suspect this might be because I process visual cues more quickly than I recall specific keystrokes for each workspace. Alternatively, it could simply be due to my familiarity with the Alt+Tab behavior from Windows and macOS, which has influenced my preferences and habits.

Visit product View on Hacker NewsMar 13, 2024

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

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.

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

55%55% 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

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

nativeThis product was originally launched on this platform.

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

41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% 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

8%8% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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