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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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GitFluence
✓ verifiedFree
Free AI helper that turns a plain-English description of a task into the matching Git command to copy and run.
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Sequel
✓ verifiedFreemium
Governed data layer connecting marketing, product and finance sources to AI agents for plain-language querying.
6.4K visits/mo4.3K saves
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Watsonx.data
✓ verifiedFree trial
IBM's open, hybrid data lakehouse that connects, governs and optimizes enterprise data to make it AI-ready across clouds and on-premises.
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
No public pricing
Free trial available
Core features
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦Natural-language to Git command suggestions
- ✦AI-driven command matching
- ✦Copy-ready command output
- ✦Git guides and reference
- ✦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
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- ✦Open hybrid data lakehouse
- ✦Connects data across clouds and on-prem
- ✦Governance, lineage and access controls
- ✦Business-context enrichment
- ✦AI-ready data for analytics and models
Use cases
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Find the correct Git command quickly
- →Learn Git syntax by describing a goal
- →Avoid memorizing Git flags
- →Marketing teams asking AI agents for campaign or ROAS reports
- →Data teams governing access to metrics across tools
- →Agencies building AI-driven client reporting
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- →Unifying fragmented enterprise data
- →Governing data for AI workloads
- →Moving AI pilots to production
- →Powering analytics with trusted data
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