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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.
AI interview copilot delivering real-time answer suggestions and live transcription across video, phone and coding interviews.
Spreadsheet-style Python data tool inside Jupyter that generates code via AI so analysts and data scientists can skip hand-coding.
Free tool that auto-generates conversational, browsable documentation for any public GitHub repo, from the makers of Devin.
No public pricing
No public pricing
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
- ✦Coding education platform for beginners
- ✦Curriculum on Next.js, Vercel, and AI
- ✦AI-powered app development
- ✦Live events and hackathons
- ✦Coding community
- ✦AI-centric platform for software engineers
- ✦Nia AI for code understanding
- ✦Context management and codebase understanding tools
- ✦Real-time answer suggestions
- ✦Live interview transcription
- ✦STAR and bullet response formats
- ✦Chrome extension for online assessments
- ✦AI interview prep and mock interviews
- ✦Multiple AI models (GPT, Claude, Gemini)
- ✦Spreadsheet interface inside Jupyter notebooks
- ✦AI-generated Python code from spreadsheet actions
- ✦Runs on customer infrastructure (no data sent to Mito)
- ✦Bring-your-own LLM API keys
- ✦Excel-to-Python conversion
- ✦Compatible with existing Jupyter extensions
- ✦AI-generated documentation for GitHub repos
- ✦Conversational Q&A about a codebase
- ✦Browsable index of popular repositories
- ✦Deep code indexing via Devin
- →Building real-time reactive apps
- →Backends for AI agents
- →Replacing Firebase or Supabase
- →Full-stack TypeScript development
- →Learning to code and build AI-powered applications
- →Developing AI agents that can work with code safely and effectively
- →Empowering developers to orchestrate AI agents across the software lifecycle
- →Improving context management and codebase understanding for AI agents
- →Getting live help during interviews
- →Answering online coding assessments
- →Practising with AI-generated questions
- →Handling phone and video interviews
- →Analysts automating Excel-style reports without hand-writing code
- →Data scientists speeding up exploratory data analysis
- →ML engineers iterating on feature engineering in notebooks
- →Enterprises needing private, self-hosted AI coding assistance
- →Understanding an unfamiliar codebase quickly
- →Onboarding to open-source projects
- →Answering questions about repo internals