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VS Code extension letting developers chat with their own custom OpenAI assistants without leaving the editor.
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
Data lab providing expert human data, RL environments, and contextual evaluations to train and assess AI models and agents.
AI coding platform routing many agents and models through one encrypted, usage-based endpoint with CLI, IDE and multi-agent execution.
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)
- ✦in-editor chat with OpenAI assistants
- ✦workspace source-code context sharing
- ✦support for custom, user-defined assistants
- ✦secure management of the user's OpenAI account
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Realm: RL environments and frontier evaluations
- ✦Cortex: contextual evaluation for production AI agents
- ✦Expert-demonstrated robotics training data
- ✦Benchmarks such as LongExtractionBench
- ✦Expert human data partnerships
- ✦Research lab on human data markets
- ✦Unified encrypted inference endpoint
- ✦Multi-agent parallel execution
- ✦CLI, IDE, and API access
- ✦App builder and remote coding agents
- ✦Chairman LLM output evaluation
- ✦35+ IDE integrations
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →getting coding help without switching out of VS Code
- →using a personalized OpenAI assistant tuned to a project
- →quick in-editor Q&A while writing code
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Train and evaluate frontier AI models
- →Improve agent performance in production
- →Source expert human data for AI labs
- →Gather demonstration data for robotics
- →Automating refactors, tests, and migrations
- →Running competing AI coding agents
- →Building apps from prompts
- →Integrating agents into CI/CD