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AI Agents Infrastructure__ai Agents__coding Agents__multi Agent OrchestrationAI Agents Infrastructure__ai Agents__coding AgentsData Analytics__data Labeling Training DataSoftware Development__code Generation__full Stack App GenerationSoftware Development__code GenerationData AnalyticsAI Agents Infrastructure__ai AgentsSoftware Development
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Aide Dev
✓ verifiedPaid
Aide helps developers code faster with parallel agents and automated workflows.
👁 7.6K/mo
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Angular.dev
✓ verifiedFree
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
👁 1.1M/mo
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Labelbox
✓ verifiedFreemium
Data-labeling and RL data platform supplying training data, environments and evaluation for frontier AI labs and enterprises.
👁 1.1M/mo
Pricing
No public pricing
Standard: $49 per month
No public pricing
No public pricing
Core features
- ✦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)
- ✦Parallel Agents for faster coding
- ✦GitHub native integration
- ✦Automated PR workflow
- ✦Smart PR suggestions
- ✦Automatic code reviews
- ✦Real-time progress tracking
- ✦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)
Use cases
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Automating code reviews
- →Generating PRs automatically
- →Improving code quality through continuous improvements
- →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
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