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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Data-labeling and RL data platform supplying training data, environments and evaluation for frontier AI labs and enterprises.
Open-source data-labeling and AI-evaluation platform for image, text, audio, video and LLM workflows, with a paid enterprise tier.
No public pricing
No public pricing
No public pricing
Free trial available
- ✦Custom AI chatbot creation
- ✦Data-driven training (URLs, PDFs, CSVs, Q&A)
- ✦Multi-language support (90+ languages)
- ✦Integration with CRMs, APIs, and tools like Zapier & Google Drive
- ✦Human handoff feature
- ✦Actions & Automation
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦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
- ✦Data labeling across modalities
- ✦RL environments and reward signals
- ✦Custom model evaluations and benchmarks
- ✦Human preference/annotation from an expert network
- ✦Recursion RL platform for enterprise agents
- ✦Robotics data (video, trajectories)
- ✦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
- →Lead generation and qualification
- →E-commerce sales assistance
- →Healthcare patient support
- →SaaS customer service
- →Banking assistance
- →Retail personalized shopping
- →Hospitality concierge services
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Building training and evaluation datasets
- →Post-training and RLHF for models
- →Benchmarking model capability
- →Training enterprise specialist agents
- →Labeling training data across modalities
- →Human-in-the-loop AI evaluation
- →RLHF and fine-tuning data collection
- →RAG and LLM benchmarking