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✓ verifiedFreemium
Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
775K visits/mo
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Wren AI Cloud
✓ verifiedFreemium
Open-source GenBI platform that turns plain-English questions into governed SQL, charts and dashboards for data teams.
43K visits/mo2.1K saves
Pricing
No public pricing
No public pricing
No public pricing
No public pricing
Free: $0/mo (20 monthly credits, 2 projects)
Essential: $179/mo (13,200 annual credits, unlimited projects)
Enterprise: $559/mo (24,000 annual credits, row/column controls)
Core features
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- ✦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)
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦Zero-ETL data integration
- ✦Federated Query
- ✦Streaming Ingestion
- ✦Instant Replication with CDC
- ✦API to SQL conversion
- ✦NoSQL to SQL conversion
- ✦SQL to API conversion
- ✦Self-service Integration
- ✦Generate SQL with AI
- ✦Natural-language to SQL with instant charts
- ✦Semantic modeling layer (MDL)
- ✦Row-level and column-level data policies
- ✦20+ connectors (BigQuery, PostgreSQL, ClickHouse, Redshift)
- ✦Auto-generated GenBI dashboards
- ✦Embedded AI API with agent skills and memory
- ✦Cloud and self-hosted deployment
Use cases
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- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Query data directly from its source in real-time.
- →Process data wherever it is, blending data from different sources.
- →Ingest streaming data from Kafka, Segment, etc., into Peaka BI Table.
- →Replace nightly batch ingestion with real-time data access.
- →Treat every data source like a relational database by converting APIs to tables.
- →Use SQL to query NoSQL databases.
- →Query consolidated data and expose it with APIs.
- →Self-serve analytics for non-technical teams
- →Building governed dashboards from a prompt
- →Embedding AI analytics into products
- →Cutting ad-hoc SQL requests to data teams
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