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Sllm.nvim – Integrate Simon’s LLM cli into Neovim (500 LOC Lua)

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Sllm.nvim – Integrate Simon’s LLM cli into Neovim (500 LOC Lua)

Hi HN, I built [sllm.nvim]( https://github.com/mozanunal/sllm.nvim ), a minimal (about 500 lines of Lua) Neovim plugin to bring Simon Willison’s excellent `llm` CLI directly into your coding workflow. - Chat with LLMs (OpenAI, OpenRouter, etc.) in a split buffer. - Add files, visual selections, shell command outputs, LSP diagnostics, or URLs as fragments to your LLM context, all from Neovim. - Async streaming jobs — never block the editor. - Switch LLM models, see token/cost stats, and use keybindings for everything. Inspired by Simon’s blog posts on long-context LLM workflows and managing context/fragments from the terminal — I wanted to make it seamless directly inside the editor. It uses [mini.nvim]( https://github.com/echasnovski/mini.nvim ) for the UI, but the core logic is just ~500 LOC. Feedback/questions welcome! Thanks to Simon Willison & the llm community for all the inspiration.

Visit productView on Hacker NewsMay 21, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, context · Missing: mac, agents, macos
93%93% 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
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released
66%66% 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
43%43% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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