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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.

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✓ verifiedFreemium

Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.

👁 775K/mo
Jam
✓ verifiedFreemium

One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.

👁 730K/mo2.9K

Thin 'Lingbot-map' agent listing on github.com with zero traffic; too thin to tell.

5.2K
Refraction.dev
✓ verifiedFreemium

AI code-generation tool creating tests, docs and refactors for developers.

👁 2.8K/mo
Pricing

No public pricing

Free: $0 (30 Jams/mo, 5 recording links)
Team: $14/creator per month billed yearly (unlimited Jams)

Free trial available

No public pricing

Hobby: Free
Pro: $8 per month
Team: $14 per user per month
Pro: $80 per year
Team: $140 per user per year

No public pricing

Core features
  • Open-source AI code assistant
  • Customizable autocomplete
  • In-editor AI chat
  • Community-built coding agent
  • One-click bug capture via browser extension
  • Automatic repro steps
  • Console, network and device logs
  • Instant replay of recent activity
  • Backend tracing and an AI debugger
  • Integrations with Jira, Linear, GitHub and Slack
  • 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)
  • Code generation in 56 languages
  • Unit test generation
  • Code refactoring
  • Inline documentation creation
  • Bug detection
  • Code conversion between languages
  • Function creation
  • CSP generation
  • CSS style conversion
  • Debug statement addition
Use cases
  • Get AI code completions while coding
  • Ask questions about code in the editor
  • Build on an open-source coding-agent foundation
  • Filing detailed bug reports
  • Reproducing issues faster in QA
  • Sharing debug context with engineers
  • Triaging support bug reports
  • Creating and manipulating datasets
  • Training small machine learning models
  • Implementing advanced training and inference logic
  • Building applications that require tensor computations
  • Generating unit tests for existing codebases
  • Refactoring legacy code to modern practices
  • Creating inline documentation for better code understanding
  • Converting code from one language to another
  • Generating SQL queries based on requirements
  • Creating CI/CD pipelines for automated deployment
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