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All-in-one no-code platform combining forms, workflow automation, AI agents, a database and email in a single subscription.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Open-source data-labeling and AI-evaluation platform for image, text, audio, video and LLM workflows, with a paid enterprise tier.
AI prototyping tool that generates UI matching your design system, letting product teams test features fast.
No public pricing
No public pricing
Free trial available
No public pricing
- ✦Drag-and-drop form builder with conditional logic
- ✦Workflow automation with 60+ node types and 400+ integrations
- ✦Prebuilt and custom AI agents for tasks like lead scoring
- ✦Relational database with AI-enriched columns
- ✦Drag-and-drop email builder with AI-drafted content
- ✦Company and contact enrichment and web research tools
- ✦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)
- ✦Signals-based fine-grained reactivity
- ✦Built-in control flow and deferrable views
- ✦Server-side rendering and hydration
- ✦First-party routing, forms and dependency injection
- ✦AI-forward tooling and MCP resources
- ✦In-browser tutorials and playground
- ✦Open-source multi-type labeling
- ✦Programmable, customizable interfaces
- ✦API, SDK and webhooks
- ✦ML backend for pre-labeling and active learning
- ✦LLM evaluation and RLHF workflows
- ✦Enterprise QA, SSO and analytics
- ✦AI UI generation from prompts
- ✦Match existing styling and design systems
- ✦Rapid, high-fidelity prototyping
- ✦Live team editing and sharing
- ✦Enterprise security and compliance
- →Capturing and automatically routing sales leads
- →Building onboarding or support-triage workflows
- →Running AI-driven lead scoring and qualification
- →Sending personalized, data-merged email campaigns
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Labeling training data across modalities
- →Human-in-the-loop AI evaluation
- →RLHF and fine-tuning data collection
- →RAG and LLM benchmarking
- →Prototype new product features
- →Test designs with customers
- →Build design-system-consistent mockups