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TypeScript backend-as-a-service with a reactive database, server functions, auth and file storage for full-stack and AI apps.
Open-source AI coding agent for VS Code, JetBrains, CLI and cloud, with 500+ models at zero inference markup and BYOK.
Online course platform teaching non-coders to build with AI tools like Cursor and Claude Code.
Prompt engineering, management, and LLM observability platform.
Developer framework and LlamaParse service for parsing documents and building AI agents and RAG workflows over them.
Free trial available
Free trial available
No public pricing
No public pricing
- ✦Reactive real-time database
- ✦TypeScript server functions (queries/mutations/actions)
- ✦Built-in authentication
- ✦Cron jobs and backend workflows
- ✦File storage, text and vector search
- ✦ACID transactions; open-source/self-host
- ✦500+ AI models at zero inference markup
- ✦Bring-your-own-keys and local model support
- ✦MIT-licensed, fully open source
- ✦Works in VS Code, JetBrains, CLI and cloud
- ✦Agent modes (Code, Architect)
- ✦Parallel isolated worktrees
- ✦Slack code reviewer and gateway
- ✦Courses on AI code editors such as Cursor
- ✦Claude Code and full-stack AI development lessons
- ✦AI agents and automation training
- ✦App-building from idea to production
- ✦Prompt engineering instruction
- ✦Magic MCP and ongoing content updates
- ✦Prompt management
- ✦Prompt evaluations
- ✦LLM observability
- ✦Team collaboration
- ✦Version control for prompts
- ✦A/B testing of prompts
- ✦Prompt Registry
- ✦Historical backtests
- ✦Regression tests
- ✦Usage monitoring
- ✦LlamaParse document parsing and extraction
- ✦Open-source framework for AI agents and workflows
- ✦Document indexing for retrieval/RAG
- ✦Prebuilt solutions by industry and use case
- ✦Free starter credits for LlamaParse
- →Building real-time reactive apps
- →Backends for AI agents
- →Replacing Firebase or Supabase
- →Full-stack TypeScript development
- →Writing and refactoring production code with AI
- →Planning features before implementation
- →Running agents across multiple IDEs and the CLI
- →Learn to code using AI assistants
- →Roll out AI tooling across an engineering team
- →Build and ship apps with no prior experience
- →Upskill in prompt engineering
- →Scaling customer support automation with LLMs
- →Empowering non-technical teams with prompt engineering
- →Building personalized AI interactions
- →Debugging LLM agents
- →Improving content creation processes
- →Managing and monitoring prompts with a team
- →Parse complex documents for AI apps
- →Build RAG and agent workflows
- →Automate invoice and claims processing
- →Search across technical documents