Vimify HN – An opinionated Vim-inspired keybinding for Hacker News

Hacker News

Hello HN! I've crafted yet another vim keybinding browser extension. While many fantastic ones exist, I felt the need to build one tailored to my preferences. Here's what stands out: - `j/k` navigation enhanced with `<number>` jumps. - Viewport keybindings like `zz/zt/zb`. - History jump with `C-o/C-i` - Scroll keybindings with `C-d/C-u/C-f/C-b`. - A Help modal that seamlessly blends with the original HN style. Thanks for checking it out, and looking forward to your feedback! Note: it's also open sourced at https://github.com/Yukaii/hackernews-vim-keybinding

Visit product View on Hacker NewsAug 16, 2023

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, hacker news · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, open · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% 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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

11%11% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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