toolspool

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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.

Pixels2Flutter logo
Pixels2Flutter
✓ verifiedFree

Turns UI screenshots into working Flutter code.

12K saves

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

5.2K saves
2.6K visits/mo
1.7K visits/mo
Quadratic Multiplayer logo
Quadratic Multiplayer
✓ verifiedFreemium

AI-native spreadsheet connecting live databases and files, letting teams query and chart data with AI, Python, or SQL.

119K visits/mo
Pricing

No public pricing

No public pricing

No public pricing

Historical Data Pack: $49.9
Base Plan: $14.9/month
Advanced Plan: $24.9/month
Enterprise Plan: $34.9/month
Pro: $18/user/month billed annually ($20 AI credits/mo)
Business: $36/user/month billed annually (2x AI credits)
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)
  • Natural language to SQL conversion
  • Commits and Pull Requests Dashboard
  • Advanced Developer Skills Analysis
  • Strategic Investment Balance Monitoring
  • Collaborative Developers Map
  • Benchmarking Comparison with Other Teams
  • Smart Notifications
  • Connects to Postgres, MySQL, Snowflake, BigQuery and other live databases
  • Imports CSV, Excel and PDF files alongside database connections
  • AI agent writes formulas, code and charts from plain-English prompts
  • Supports Python, SQL, formulas and JavaScript in the same sheet
  • Built-in connectors for QuickBooks, Google Analytics, Mixpanel and Plaid
  • MCP support so external AI agents can read/write spreadsheet cells
  • REST API for triggering AI runs and manipulating cells programmatically
  • Scheduled tasks and PDF data extraction
Use cases
  • Creating and manipulating datasets
  • Training small machine learning models
  • Implementing advanced training and inference logic
  • Building applications that require tensor computations
  • Generating SQL queries from text descriptions.
  • Visualize historical graphs of code evolution
  • Assess development team performance using RSI and EMA
  • Understand developer skills and identify areas for improvement
  • Categorize commits by type (fixes, refactoring, etc.) to analyze investment balance
  • Identify individual and collective contributors within the team
  • Compare team performance with industry benchmarks
  • Receive weekly and monthly reports with AI-extracted insights
  • Finance teams building recurring, auditable financial reports
  • Analysts combining multiple data sources into one live dashboard
  • Traders and investors tracking market and portfolio data
  • Product and sales teams generating forecasts and charts without formulas
  • Teams wanting an AI agent to verify and edit spreadsheet work directly
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