Split Vim Markdown Preview – Terminal-Based Markdown Preview for Vim

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I built this lightweight Vim plugin for developers who want to preview markdown without leaving their terminal environment. It uses https://github.com/charmbracelet/glow to render beautiful markdown directly in a Vim split window. Key features: - Toggle preview with \mp - no need to leave Vim - Beautiful terminal-based rendering with syntax highlighting - Handles code blocks (200+ languages), tables, math equations, and task lists - Split window layout keeps your workflow intact by toggling with \mp Why I built this: I love using vim to quickly edit files, write down thoughts, and update notes. Building terminal tools can sometimes be big time commitments that take me away from other work. Claude Code helped me to quickly vibe code this integration to improve my workflow. When I looked at other markdown plugins for vim, they required preview plugins requiring Node servers and browser switching, which breaks concentration when writing documentation. This keeps everything in the terminal where Vim users prefer to work, so they can stay in flow while building. The repo includes a demo GIF showing the plugin in action so you can see how it looks before installing. Installation is straightforward - just install glow and drop the plugin file in your Vim directory.

Visit product View on Hacker NewsJun 27, 2025

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

90%90% 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 · Missing: supports, reddit linkedin, podcasting

66%66% 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: io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

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

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

27%27% 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

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.

Correct prediction on native model

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