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Enterprise data-annotation and evaluation platform pairing a labeling tool with a managed expert annotator workforce.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Google Labs experiment for building and sharing AI mini-apps from natural-language prompts, no coding required.
AI chat-based builder that lets non-developers describe an app and generate native mobile apps to publish to app stores.
No public pricing
No public pricing
No public pricing
No public pricing
No public pricing
- ✦CSV to API conversion
- ✦Data parsing (CSV to JSON)
- ✦Filtering capabilities
- ✦Customizable multimodal annotation editors for image, video, text and audio
- ✦Support for RLHF preference data, SFT datasets, RAG and agent evaluation workflows
- ✦Managed expert annotator workforce option
- ✦Data curation, exploration and analytics tools
- ✦Team and project management with SSO on higher tiers
- ✦Integrations with AWS, GCP, Databricks, Snowflake and others
- ✦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
- ✦Build AI mini-apps from natural-language prompts
- ✦Visual editor for prompt/tool workflows
- ✦Share created apps with others
- ✦No-code AI app prototyping
- ✦Chat-based app generation from a natural-language description
- ✦Native iOS app output that can be published to the App Store
- ✦Native game generation including 3D worlds and multiplayer
- ✦File uploads to guide generation (larger uploads on paid tiers)
- ✦Import paths from other builders/tools (e.g., Lovable, GitHub)
- →Sharing CSV data with a team via an API
- →Creating a public API from CSV data
- →Filtering and accessing specific data within a CSV file programmatically
- →Building large-scale labeled datasets to train computer vision or NLP models
- →Running human evaluation and RLHF pipelines for LLM fine-tuning
- →Auditing and scoring AI agent decisions with human review
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Prototyping an AI workflow quickly
- →Sharing a custom AI mini-app
- →Automating a task with chained prompts
- →Non-developers building and shipping a mobile app idea
- →Indie creators prototyping and monetizing app-store apps
- →Building simple multiplayer or 3D games without coding
- →Converting an existing web project or repo into a mobile app