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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.

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

5.2K saves
Cursor logo
Cursor
✓ verifiedFreemium

AI coding agent and editor that understands your codebase, runs autonomous agents and edits across your stack.

4.6M visits/mo102K saves
Pixels2Flutter logo
Pixels2Flutter
✓ verifiedFree

Turns UI screenshots into working Flutter code.

12K saves
Qoder logo
Qoder
✓ verifiedFreemium

Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.

2.7M visits/mo32K saves
Jules by Google logo
Jules by Google
✓ verifiedFreemium

Google's asynchronous AI coding agent that autonomously fixes bugs and builds features in GitHub repos, powered by Gemini.

Pricing

No public pricing

Hobby: Free
Pro (Individual): $20/mo
Teams: $40/user/mo

No public pricing

No public pricing

Free trial available

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)
  • Codebase-aware AI completions and edits
  • Autonomous and cloud agents
  • Access to frontier models (GPT, Claude, Gemini, Grok)
  • CLI, Slack and GitHub integration
  • Scheduled automations and triggers
  • Team marketplace and enterprise controls
  • Multi-agent collaboration for end-to-end tasks
  • Persistent memory and custom rules
  • Extensible skills and plugins
  • Rich context across code, images, and directories
  • Automatic codebase documentation generation
  • Terminal-native CLI and JetBrains IDE plugin
  • Cloud-hosted agents for enterprise use
  • Autonomous coding agent
  • GitHub repository integration
  • Runs in a cloud VM
  • Multi-step task planning
  • Opens pull requests with changes
  • Powered by Gemini
Use cases
  • Creating and manipulating datasets
  • Training small machine learning models
  • Implementing advanced training and inference logic
  • Building applications that require tensor computations
  • AI pair programming
  • Multi-file refactors
  • Autonomous feature building
  • Automated PR reviews and CI fixes
  • Autonomous feature development in large codebases
  • Terminal-based AI pair programming
  • Cross-department task automation for legal, finance, HR
  • Onboarding developers to unfamiliar codebases
  • Fixing bugs asynchronously
  • Adding features to a codebase
  • Writing and updating tests
  • Automating routine development tasks
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