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AI video social-listening platform that scans social video content to surface untagged brand mentions, sentiment, and creator leads.
Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
Continuously analyzes MySQL, MariaDB, and PostgreSQL workloads to recommend and safely apply configuration and query fixes.
AI coding assistant that gathers project context to plan, generate, test and ship code across the SDLC via IDE and chat integrations.
No public pricing
No public pricing
Free trial available
Free trial available
- ✦AI video analysis of audio, visuals, and captions
- ✦Untagged brand mention and UGC detection
- ✦Creator sourcing by niche, audience, and brand fit
- ✦Outbound comment engagement tracking
- ✦Competitive benchmarking across brands
- ✦Brand sentiment and safety monitoring
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦Workload-based configuration tuning
- ✦SQL query analytics and optimization suggestions
- ✦Schema optimization (duplicate/unused index detection)
- ✦24/7 automated health and security monitoring
- ✦One-command agent installation
- ✦Human approval required before applying changes
- ✦Automatic context-gathering from connected engineering sources
- ✦AI-generated code, tests and pull requests from tickets
- ✦Task planning that breaks complex work into subtasks
- ✦Auto-updating engineering documentation
- ✦Vector search over embedded project data
- ✦Multiple selectable AI models (GPT, Gemini, Claude, Llama, etc.)
- ✦Engineering productivity analytics dashboard
- →Finding untagged user-generated content about a brand
- →Sourcing creators that match specific audience criteria
- →Measuring campaign impact across social video
- →Benchmarking a brand's social presence against competitors
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Database teams reducing manual tuning workload
- →Hosting providers optimizing customer databases at scale
- →Engineering teams without a dedicated DBA fixing performance issues
- →AWS RDS users tuning managed database instances
- →Engineering teams automating ticket-to-PR workflows
- →Developers wanting AI-assisted debugging and test generation
- →Engineering managers tracking AI-driven productivity gains
- →Teams centralizing documentation from scattered sources