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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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Artificial Analysis
✓ verifiedFreemium
Independent benchmarks comparing AI models and API providers on intelligence, speed, and cost across many leaderboards.
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Vespa
✓ verifiedFree trial
Open-source AI search and vector database platform for building large-scale search, RAG, and recommendation systems.
Pricing
No public pricing
Free: $0/contributor (up to 7 contributors, 90-day retention)
Premium: $10/contributor (unlimited contributors, AI insights)
No public pricing
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
- ✦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
- ✦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)
- ✦Intelligence Index across many benchmarks
- ✦Model speed and cost comparisons
- ✦Coding, speech, image, and video leaderboards
- ✦Provider performance analysis
- ✦Personalized model recommender
- ✦Premium data and reports
- ✦Combined vector, text, and structured search
- ✦Distributed machine-learned ranking at query time
- ✦Streaming search mode for cost-efficient personal/private data
- ✦Support for retrieval-augmented generation pipelines
- ✦Continuous deployment and automated scaling
- ✦Open-source core with a managed cloud option
Use cases
- →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
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Choosing an AI model or provider
- →Tracking frontier model progress
- →Comparing price and performance
- →Building large-scale enterprise search engines
- →Powering RAG pipelines that need strong retrieval relevance
- →Building recommendation and ad-targeting systems
- →Search over personal/private data at lower indexing cost
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