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VimLM – A Local, Offline Coding Assistant for Vim

Hacker News

VimLM – A Local, Offline Coding Assistant for Vim

VimLM is a local, offline coding assistant for Vim. It’s like Copilot but runs entirely on your machine—no APIs, no tracking, no cloud. - Deep Context: Understands your codebase (current file, selections, references). - Conversational: Iterate with follow-ups like "Add error handling". - Vim-Native: Keybindings like `Ctrl-l` for prompts, `Ctrl-p` to replace code. - Inline Commands: `!include` files, `!deploy` code, `!continue` long responses. Perfect for privacy-conscious devs or air-gapped environments. Try it: ``` pip install vimlm vimlm ``` [GitHub]( https://github.com/JosefAlbers/VimLM )

Visit product View on Hacker NewsFeb 14, 2025

Actual performance

93points

18comments

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

89%89% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

56%56% 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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

24%24% 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

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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