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Text2SQL
✓ verifiedPaid
AI tool that converts natural-language questions into SQL queries, sold via a Lemon Squeezy storefront with tiered pricing.
20K visits/mo14K saves
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devActivity
✓ verifiedFreemium
GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
52K visits/mo
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Sherloq
✓ verifiedFreemium
AI chat assistant for analysts that generates and fixes SQL using an organization's own saved query history.
12K visits/mo
Pricing
No public pricing
Text2SQL.AI: $7.00-$48.00
Text2SQL.AI Pro: $29.00-$228.00
Free trial available
Free: $0/contributor (up to 7 contributors, 90-day retention)
Premium: $10/contributor (unlimited contributors, AI insights)
Free Plan: $0 one-time
Hobby: $16/month
Standard: $83/month
Growth: $333/month
Auto Recharge Credits: $11/mo for 1000 credits
Credit Pack: $9/mo for 1000 credits
Enterprise Plan: Contact for 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)
- ✦Natural language to SQL query generation
- ✦Standard and Pro subscription tiers
- ✦Checkout and billing via Lemon Squeezy
- ✦Contribution and work-quality analytics
- ✦Automated, AI-powered performance reviews
- ✦Retrospective insights
- ✦Operational bottleneck alerts
- ✦Gamification with XP, levels and leaderboards
- ✦Uses Git metadata without accessing source code
- ✦Web scraping
- ✦Web crawling
- ✦Data extraction in Markdown, JSON, and screenshot formats
- ✦Dynamic content handling
- ✦Rotating proxies
- ✦Rate limits management
- ✦Open-source availability
- ✦Media Parsing
- ✦AI chat trained on the user's own SQL repository
- ✦query saving, tagging, and versioning
- ✦team folder permissions and sharing
- ✦Chrome extension and IntelliJ plugin
- ✦SOC2-compliant security with no database access needed
- ✦table and field lookup assistance
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 without writing raw syntax
- →Helping non-technical users query databases
- →Speeding up ad hoc data lookups for analysts
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Powering AI assistants with real-time web content
- →Enhancing sales data with web information
- →Adding scraping capabilities to code editors
- →Enabling customers to build AI apps with web data
- →Extracting comprehensive information for in-depth research
- →sharing reusable SQL snippets across an analytics team
- →quickly fixing syntax errors in existing queries
- →onboarding new analysts to a team's existing SQL logic
- →building a searchable personal or team SQL knowledge base
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