Avante.nvim: Use Your Neovim Like Using Cursor AI IDE
Avante.nvim is a Neovim plugin offering AI-driven code suggestions, allowing users to apply changes easily. It requires lazy.nvim for installation and plans future enhancements for AI interactions and integration.
Read original articleThe GitHub repository avante.nvim is a Neovim plugin designed to provide AI-driven code suggestions, mimicking the functionality of the Cursor AI IDE. Key features include AI-powered code assistance, allowing users to receive suggestions for code improvements, and a one-click application feature that enables users to apply suggested changes directly to their code. Installation can be done using lazy.nvim, with a provided configuration that includes dependencies such as nvim-web-devicons and dressing.nvim. Users can interact with the AI by opening a code file in Neovim and using the `:AvanteAsk` command to receive and apply suggestions. Key bindings facilitate easy access to features, including showing a sidebar and refreshing suggestions. The roadmap for the plugin includes plans for enhanced AI interactions and better integration with Language Server Protocol (LSP) and Tree-sitter for improved code suggestions. The plugin is licensed under the Apache License, and further details can be found on its GitHub repository.
- avante.nvim is an AI-driven code suggestion plugin for Neovim.
- Users can apply AI suggestions with a single command.
- Installation requires lazy.nvim and includes several dependencies.
- Key bindings are available for easy navigation and suggestion management.
- Future updates aim to enhance AI interactions and integrate with LSP and Tree-sitter.
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- Users express a preference for open-source alternatives like zed and Continue.dev, highlighting their flexibility and features.
- Many commenters compare Avante.nvim to other tools like Cursor and Cody, discussing their strengths and weaknesses.
- Concerns about installation complexity and dependency management for Avante.nvim are raised.
- Some users question the effectiveness of AI tools in understanding complex coding tasks and generating accurate suggestions.
- There is a general curiosity about the future of AI in coding and how it will impact existing tools and user workflows.
Keep in mind Cursor is just a fork of VSCode, with AI features that are pretty much just embedded extensions. Their product is great, but many users would prefer a bring-your-own-key & selecting their own model providers.
zed: https://zed.dev/
HN Discussion from few days ago (397 pts): https://news.ycombinator.com/item?id=41302782
To me it feels like trying to explain something (for an LLM) is harder than writing the actual code. Either I know what I want to do, and describing things like iteration in English is more verbose than just writing it; or I don’t know what I want to do, but then can’t coherently explain it. This is related to the “rubber duck method”: trying to explain an idea actually makes one either properly understand it or find out it doesn’t make sense / isn’t worthwhile.
For people who experience the same, do tools like Cursor make you code faster? And how does the LLM handle things you overlook in the explanation: both things you overlooked in general, and things you thought were obvious or simply forgot to include? (Does it typically fill in the missing information correctly, incorrectly but it gets caught early, or incorrectly but convincing-enough that it gets overlooked as well, leading to wasted time spent debugging later?)
Curious to see how all this VC money into editors end up.
Open source tooling is always going to be a different focus: giving you a toolbox to assemble functionality yourself.
If Claude could do custom shit on rules_python, I’d marry it.
But it’s a fucking newb on hard stuff.
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