Search Results for "ask"

Ask questions or generate code with the context of your codebase for accurate results. This also works with choosing specific folders, online docs, terminal content, files, and more. PearAI can directly code in your files, and allow you to see diffs. Try CMD+I (CTRL+I on Windows). Here, we ask PearAI to help us add error handling and comments.

Continue is the leading open-source AI code assistant. You can connect any models and any context to build custom autocomplete and chat experiences inside VS Code and JetBrains. Continue enables you to use the right model for the job, whether it's open-source or commercial, running local or remote, and used for chat, autocomplete, or embeddings. And we provide numerous points of configuration so that you can customize the extension to fit into your existing workflows.

Amazon Q Developer CLI brings an agentic, chat-driven coding assistant to your terminal so you can ask for help, generate code, and perform routine dev tasks with natural language. It blends knowledge of your local workspace with command-line context to suggest commands, explain flags, and scaffold files or workflows. The tool aims to shorten the gap between intent and action by letting you request operations like creating a test, refactoring a function, or drafting a Dockerfile without leaving the shell. ...

avante.nvim is a Neovim plugin designed to emulate the behaviour of the Cursor AI IDE. It provides users with AI-driven code suggestions and the ability to apply these recommendations directly to their source files with minimal effort.

aiac is a command line tool to generate IaC (Infrastructure as Code) templates, configurations, utilities, queries and more via OpenAI's API. The CLI allows you to ask the model to generate templates for different scenarios (e.g. "get terraform for AWS EC2"). It will make the request, and store the resulting code to a file, or simply print it to standard output. By default, aiac uses the same model used by ChatGPT, but allows using different models.

Sourcebot is a self-hosted code intelligence platform that helps developers and AI agents understand large codebases through search, navigation, and natural language queries. It allows users to ask complex questions about their repositories and receive structured answers grounded in actual code references. The system combines fast code search with reasoning models, enabling it to traverse dependencies, follow references, and generate contextual explanations. It supports indexing multiple repositories across different platforms, making it useful for organizations with distributed codebases. ...

...Unlike traditional session-based coding tools, Letta Code attaches a long-lived agent to a working directory so that the agent accumulates memory about a project’s structure, preferences, and history, effectively acting as a collaborative partner rather than a stateless helper. Users can initialize and connect the agent to various models, including popular large language models, and issue commands, refactor code, or ask context-aware questions directly in the terminal, with memory retained across multiple interactions.

Mistral Vibe is an AI-powered “vibe-coding” command-line interface (CLI) and coding-assistant framework built by Mistral AI to let developers write, refactor, search, and manage code through natural language and context-aware automation, rather than manual typing only. It aims to take developers out of repetitive boilerplate and let them stay “in the flow”: you can ask the tool to generate functions, refactor code, search across the codebase, manipulate files, commit changes via Git, or run commands — all from a unified CLI interface. Behind the scenes, it leverages Mistral’s coding-optimized LLM stack (including models tuned for code understanding and generation), with project-wide context awareness: it scans your file structure, Git status, and recent history to inform suggestions so that generated code aligns with existing context.

While coding with AI assist, I noticed bigger projects need something more rigid as context so the AI coder doesn't stray away when I try to add new features. You know, to keeps its memory fresh and not change the stuff that already works. The trick is to ask the AI to write a diagram in Mermaid.js format that solidifies the structure of the project and then use that as context to keep the AI reminded at all times what the project is as a whole. This will prevent it from changing things its not supposed to change. This standalone offline web app will convert that mermaid.js code into a visual SVG image so that YOU as a human will be able to understand what the AI think about the structure of your projects so you can see it and fix any misconceptions until the diagram is correct for your project. ...