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CLI-based personal AI that runs 15+ LLMs from your terminal and pairs with Markdown tools like Obsidian and VS Code.
AI coding assistant that gathers project context to plan, generate, test and ship code across the SDLC via IDE and chat integrations.
Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.
AI code-review and context tool that maps a codebase into a knowledge graph so coding agents and PR reviews stay grounded in real context.
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- ✦Terminal/CLI interface for prompting LLMs
- ✦Access to 15 models across 7 providers
- ✦Markdown workflow integration (Obsidian, VS Code, MkDocs)
- ✦Situational web-app generation and local run
- ✦Web scraping/content extraction (gather)
- ✦Vision-model support for images
- ✦Configurable prompt templates and intents
- ✦Automatic context-gathering from connected engineering sources
- ✦AI-generated code, tests and pull requests from tickets
- ✦Task planning that breaks complex work into subtasks
- ✦Auto-updating engineering documentation
- ✦Vector search over embedded project data
- ✦Multiple selectable AI models (GPT, Gemini, Claude, Llama, etc.)
- ✦Engineering productivity analytics dashboard
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦Codebase-aware developer chat
- ✦AI code completions and inline edits
- ✦Customizable and shareable prompts
- ✦Automatic bug identification and debugging help
- ✦Context filters to exclude sensitive repos
- ✦Integrates with major code hosts and IDEs
- ✦AI Architect knowledge graph of the codebase
- ✦Codebase-aware AI code reviews
- ✦Grounded coding and technical design docs
- ✦Impact and feasibility assessment
- ✦Integrations: Cursor, Claude Code, Copilot, Codex, Jira, Slack
- ✦Self-hosted deployment option
- →Prompt and compare multiple LLMs from the terminal
- →Generate and run small web apps without setup
- →Automate Markdown document workflows
- →Extract structured content from text and webpages
- →Engineering teams automating ticket-to-PR workflows
- →Developers wanting AI-assisted debugging and test generation
- →Engineering managers tracking AI-driven productivity gains
- →Teams centralizing documentation from scattered sources
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Engineers asking questions about an unfamiliar large codebase
- →Teams standardizing common coding tasks with shared prompts
- →Developers debugging errors faster with AI-assisted context
- →Enterprises running large-scale code migrations
- →Automated code review
- →Context-grounded AI coding
- →Technical design and scoping
- →Cutting agent token costs