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AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
Data-labeling and RL data platform supplying training data, environments and evaluation for frontier AI labs and enterprises.
Outlier is a platform where experts earn freelance income training and evaluating AI by writing prompts, rubrics and rating answers.
No public pricing
No public pricing
Free trial available
No public pricing
No public pricing
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)
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Chat inside GitHub issues and PRs
- ✦Task-to-implementation plans with code
- ✦Automatic bug-fix suggestions
- ✦Pull-request summaries for faster review
- ✦Full-codebase context
- ✦GitHub-native integration
- ✦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)
- ✦Remote AI-training gig work
- ✦Tasks: prompt writing, rubrics, rating answers
- ✦Flexible hours, work from anywhere
- ✦Weekly, quality-based pay
- ✦Roles across coding, STEM and languages
- ✦Free access to paid AI models
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Speeding up pull-request reviews
- →Implementing features from task descriptions
- →Debugging with AI-proposed solutions
- →Answering questions about a repo
- →Boosting a solo developer's output
- →Building training and evaluation datasets
- →Post-training and RLHF for models
- →Benchmarking model capability
- →Training enterprise specialist agents
- →Earning side income as an AI trainer
- →Contributing expertise to improve AI
- →Flexible remote work for grads and experts
- →Gaining hands-on AI and prompt experience