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Vimacs – Fast, Feature-rich & Beautiful Neovim configuration

Hacker News

Vimacs – Fast, Feature-rich & Beautiful Neovim configuration

Vimacs project has just released! One of the most feature-rich neovim configurations out there. Manifest: There are some philosophical differences between (neo)vim & emacs (barely summarizing): (neo)vim is more minimalist and more focused on coding (unix philosophy) emacs is known to be an extensible editor that can ease the process of (almost) any text related task. (IRC, E-Mail, Note taking etc.) With Vimacs you have the efficiency of (neo)vim and (some) features of emacs. - Feature-complete (Some inspired from emacs & JetBrains) - Fast (Lazy loading ~%97.5) - Simple (configuration + usage) Vimacs some out of the box features (full list: [0]): > NOTE: This is 1/10 of the repository's list - Debugger (Polished for the best) - Profiler - Hot-reloaded themes - Magit + Github integration - On-Click updates - Project Manager (Github templates + hooks!) - Markdown tools - Code refactoring tools - Tree-sitter actions (e.g JetBrains extract function, unjoin line etc.) - Magit + Github Integration - Task framework - Testing framework - File Manager (dired like) - Leetcode Client - Org-mode - Compiler Explorer (for any language!) - Codegen tools - Refactoring tools - Pastebin Client - Smart Snippets - AI tools (assistant, code-generation, inline hints) - Multiple UI Modes for saner code editing experience Vimacs has some optional features like: - IRC Client - E-Mail Client - Discord Client - Internet Browser - And more! [0] For more information (and a feature list) please visit repository homepage [0] --------------------------------------- [0]: https://github.com/UTFeight/vimacs

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, coding, code · Missing: agents, macos, agent
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: just released, io · Missing: https docs, excited, exist
73%73% 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
45%45% 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
28%28% 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
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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