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AI-powered IDE with code completion, generation, explanation and debugging, plus a cloud dev environment, for developers.
AI agent-based end-to-end testing platform for SaaS teams that runs exploratory and PR-triggered tests without maintaining test scripts.
One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.
Low-code integration platform for connecting thousands of APIs into workflows and AI agents, including an MCP tool server.
No public pricing
No public pricing
No public pricing
Free trial available
No public pricing
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦AI code completion and snippet generation
- ✦Natural-language code generation
- ✦Code explanation and AI Q&A
- ✦Automated bug detection and fixes
- ✦Zero-config cloud development environment
- ✦Project creation from templates or Git
- ✦AI agents that visually explore and test UI like a real user
- ✦Automatic PR-triggered test runs via GitHub/Vercel preview integration
- ✦Self-healing tests that adapt to UI and workflow changes
- ✦Mobile web, iOS, and Android app testing support
- ✦Detailed debugging with screenshots, logs, and failure reasoning
- ✦Cloud-native execution with no source-code access required
- ✦One-click bug capture via browser extension
- ✦Automatic repro steps
- ✦Console, network and device logs
- ✦Instant replay of recent activity
- ✦Backend tracing and an AI debugger
- ✦Integrations with Jira, Linear, GitHub and Slack
- ✦Visual and code-based workflow builder
- ✦Prebuilt AI agent builder and deployment
- ✦Managed authentication across thousands of apps
- ✦MCP server exposing integrations as agent tools
- ✦Scheduled and event-triggered workflows
- ✦Connect SDK for embedding integrations into other products
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Writing and completing code faster with AI
- →Onboarding to unfamiliar codebases
- →Debugging and optimizing code
- →Spinning up dev environments in the browser
- →Engineering teams wanting regression testing without maintaining scripts
- →SaaS companies needing continuous QA feedback on every pull request
- →Teams replacing manual QA hours with automated agent-driven testing
- →Filing detailed bug reports
- →Reproducing issues faster in QA
- →Sharing debug context with engineers
- →Triaging support bug reports
- →Building AI agents that call external APIs and tools
- →Automating cross-app workflows such as Slack, Gmail, or Sheets notifications
- →Embedding third-party integrations into a SaaS product
- →Prototyping event-driven automations without heavy infrastructure