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WeakVim, My attempt at a modular, transparent starter kit for Neovim

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WeakVim, My attempt at a modular, transparent starter kit for Neovim

Hi all, I _love_ Neovim. However, I think many parts of it could be easier to configure or understand, especially when it comes to LSPs. There's a ton of plugins that fill the gap, but some of them are sparse in documentation. There's also distributions like LunarVim that attempt to add a Doom Emacs-like layer above. My approach is a bit different, simply giving an opinionated, modular, and documented configuration that an interested user can easily modify to their needs. It makes use of the most helpful plugins I've encountered to create the best experience I could muster while retaining an air of simplicity. I hope this is helpful to someone out there!

Visit productView on Hacker NewsApr 29, 2022
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2points
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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% 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 HuntOn track for Day 1 leaderboard · Strong signals: mac, user · Missing: agents, macos, agent
58%58% 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
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
40%40% 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
35%35% 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
12%12% 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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