Official tutorial hub teaching how to code effectively with AI agents inside Cursor, from AI foundations to working with agents and reviewing their output. Lessons cover rules, tools, context as working memory, and which tasks agents handle well.
Turns natural-language tasks into edits, reviews, and agent runs inside a developer workspace. It pairs codebase indexing with model choice across editor, CLI, cloud agents, and PR review.
Bundles AI features and coding agents into JetBrains IDEs, using IDE code intelligence for completion, refactoring, and chat. Runs on the proprietary Mellum model or your choice of OpenAI, Gemini, Anthropic, and local models via Ollama or LM Studio.
GPU-accelerated code editor written in Rust, organized like a game engine to render its UI via shaders. Includes native agentic coding over the open Agent Client Protocol, multiplayer editing, LSP/DAP, and an open edit-prediction model.
Visually edit Next.js + Tailwind projects in the browser like Figma, with every change written straight back to your real React code. Pairs a DOM-level visual canvas with AI chat that scaffolds and edits components, plus branching and one-click deploy.
Reviews code in the IDE, CLI, and pull requests, flagging bugs, logic gaps, security holes, and missing tests using context from the whole repo and its dependencies. Enforces team-specific rules learned from past PRs.
Keeps the former Windsurf IDE lineage alive as Devin Desktop, a local editor for planning, delegating, reviewing, and shipping code with cloud and local agents from one surface.
Performs fast static type checking and provides a language server with code navigation, semantic highlighting, and completions for Python. Processes ~1.85M lines/sec and completes IDE rechecks typically under 10ms — intended for responsive editor workflows and large codebases.
Exposes xcodebuild, simulator, and device actions as Model Context Protocol tools, so AI agents can build, run, capture logs, and debug iOS and macOS apps without hand-written scripts. Also runs as a standalone CLI and plugs into MCP clients.
Gives coding agents symbol-level codebase access via language servers (LSP), turning cross-file renames, reference lookups, and edits into precise operations instead of fragile text search. Runs as an MCP server spanning 40+ languages.
Orchestrates AI coding agents around tasks, sessions, artifacts, reviews, and parallel Claude Code workflows so teams can manage complex codebase work with more visibility.
Provides semantic code search for AI coding agents by making an entire codebase available as context via hybrid BM25 + vector retrieval, reducing token costs. Uses incremental indexing, AST-based chunking, and Zilliz/Milvus-backed vectors for large-codebase and IDE workflows.