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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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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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Kaggle
✓ verifiedFree
Google-owned hub for data scientists to find datasets, enter ML competitions, run notebooks, and learn.
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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
Pricing
No public pricing
No public pricing
Free: $0/contributor (up to 7 contributors, 90-day retention)
Premium: $10/contributor (unlimited contributors, AI insights)
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)
Core features
- ✦Natural language to SQL conversion
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦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
- ✦Public dataset repository
- ✦Machine-learning competitions with prizes
- ✦Browser-based notebooks with free GPU/TPU
- ✦Micro-courses on data science topics
- ✦Community forums and shared code
- ✦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
Use cases
- →Generating SQL queries from text descriptions.
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Practicing and benchmarking ML models
- →Finding datasets for analysis
- →Competing in predictive-modeling contests
- →Learning data science skills
- →Sharing reproducible notebooks
- →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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