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SEAL Leaderboards
✓ verifiedFree
Scale AI provides training data and evaluation platforms; major AI company.
👁 625K/mo♥ 3.0K
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Code Autopilot
✓ verifiedFreemium
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
Pricing
No public pricing
No public pricing
Free trial available
No public pricing
No public pricing
No public pricing
Core features
- ✦High-quality training data for AI models
- ✦Scale Data Engine for data management and labeling
- ✦Scale GenAI Platform for full-stack Generative AI
- ✦Scale Donovan for AI-powered decision-making
- ✦AI model evaluation and red teaming
- ✦RLHF (Reinforcement Learning from Human Feedback)
- ✦Enhanced Context Engineering for deep codebase analysis and adaptive memory
- ✦Intelligent Agents for autonomous planning, coding, and testing
- ✦Spec-Driven Development for clarifying requirements and automating execution
- ✦Intelligent Codebase Search and Advanced Repository Insight
- ✦Context-aware code completions and next-edit suggestions
- ✦Support for leading AI models (Claude, GPT, Gemini)
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- ✦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 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
Use cases
- →Developing self-driving car AI with high-quality training data.
- →Building Generative AI applications using the Scale GenAI Platform.
- →Improving AI model performance through supervised fine-tuning and RLHF.
- →Evaluating the safety and robustness of AI models using SEAL Leaderboards.
- →Integrating enterprise data into foundation models for strategic differentiation.
- →Delegating complex software development tasks to AI agents for autonomous completion.
- →Performing multi-file code edits and refactoring through natural language chat.
- →Gaining deep architectural understanding of a codebase to resolve issues with precision.
- →Generating unit tests, code explanations, and uncovering codebase architecture.
- →Systematically tackling software development tasks from planning to testing.
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- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →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
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