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✓ verifiedFreemium
Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
👁 775K/mo
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Sequel
✓ verifiedFreemium
Governed data layer connecting marketing, product and finance sources to AI agents for plain-language querying.
👁 6.4K/mo♥ 4.3K
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Code Autopilot
✓ verifiedFreemium
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
Pricing
No public pricing
No public pricing
Free: $0/mo (1 data source, 1 user)
Pro: $19/mo (unlimited data sources, 1 user)
Team: $99/mo (unlimited data sources and users, Slack access)
No public pricing
Core features
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦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)
- ✦Unified connection to 100+ marketing/product/finance data sources
- ✦MCP-compatible interface usable by any AI agent
- ✦Learns custom metric definitions and joins across sources
- ✦Secure credential gateway that keeps raw keys from agents
- ✦Cross-source joins spanning databases, warehouses and product data
- ✦Fine-grained audit logs of every query
- ✦Live dashboards and debugging in plain English
- ✦Chat inside GitHub issues and PRs
- ✦Task-to-implementation plans with code
- ✦Automatic bug-fix suggestions
- ✦Pull-request summaries for faster review
- ✦Full-codebase context
- ✦GitHub-native integration
Use cases
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Marketing teams asking AI agents for campaign or ROAS reports
- →Data teams governing access to metrics across tools
- →Agencies building AI-driven client reporting
- →Speeding up pull-request reviews
- →Implementing features from task descriptions
- →Debugging with AI-proposed solutions
- →Answering questions about a repo
- →Boosting a solo developer's output
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