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Lovable
✓ verifiedFreemium
Chat-based AI builder turning ideas into full software products.
👁 35M/mo♥ 69K
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Text2SQL
✓ verifiedFreemium
Converts natural language into SQL across databases; useful real dev tool.
👁 20K/mo♥ 14K
Pricing
No public pricing
Gemini Code Assist for individuals (preview): $0/user/month
Gemini Code Assist Standard: $19/user/month (annual commitment) or $22.80/user/month (no commitment)
Gemini Code Assist Enterprise: $45/user/month (annual commitment) or $54/user/month (no commitment)
No public pricing
Free trial available
No public pricing
Basic: $8 USD / month billed annually ($4)
Pro: $25 USD / month billed annually ($19)
Enterprise: Custom USD / month
Core features
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- ✦AI-powered code completion and generation
- ✦Conversational assistant in IDEs
- ✦Automated code reviews in GitHub
- ✦Integration with Firebase, BigQuery, Apigee, and Application Integration
- ✦Customizable code suggestions based on private source code repositories (Enterprise edition)
- ✦AI-powered software development
- ✦Chat-based interface for specifying requirements
- ✦Full-stack engineering capabilities
- ✦Rapid app prototyping
- ✦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)
- ✦Text to SQL conversion
- ✦AI query generation, explanation, fixing, and optimization
- ✦Support for multiple database types
- ✦Database schema integration for accuracy
- ✦Public API for integration with other tools
Use cases
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- →Accelerate code development with AI-powered suggestions
- →Reduce code review time and improve code quality
- →Build integrations with AI assistance in various Google Cloud services
- →Generate SQL statements from natural language in Databases
- →Creating developer portfolios
- →Building real estate listings applications
- →Developing file uploaders
- →Generating slide presentations
- →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 natural language descriptions
- →Explaining complex SQL queries
- →Fixing errors in existing SQL code
- →Optimizing SQL queries for performance
- →Building custom SQL AI tools using the API
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