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Open-source AI coding agent for VS Code, JetBrains, CLI and cloud, with 500+ models at zero inference markup and BYOK.
AI social-listening and customer-feedback platform that tracks brand mentions, creators, and sentiment across video-first social platforms.
TypeScript backend-as-a-service with a reactive database, server functions, auth and file storage for full-stack and AI apps.
Local desktop app that assembles code-context prompts for LLMs, with API integrations, token tracking, and prompt saving.
Free, regularly updated comparison tool listing 47+ vector databases side by side across features, indexing, and pricing.
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- ✦500+ AI models at zero inference markup
- ✦Bring-your-own-keys and local model support
- ✦MIT-licensed, fully open source
- ✦Works in VS Code, JetBrains, CLI and cloud
- ✦Agent modes (Code, Architect)
- ✦Parallel isolated worktrees
- ✦Slack code reviewer and gateway
- ✦Cross-platform social listening including video (TikTok, Reels, Shorts)
- ✦Competitive analysis of rival brands' content and creators
- ✦Speech-to-text and AI vision analysis of video content
- ✦Plain-language query interface for filtering data
- ✦Creator discovery and campaign tracking
- ✦Centralized customer feedback intake with AI tagging and sentiment
- ✦MCP connector for using data inside Claude/ChatGPT workflows
- ✦Reactive real-time database
- ✦TypeScript server functions (queries/mutations/actions)
- ✦Built-in authentication
- ✦Cron jobs and backend workflows
- ✦File storage, text and vector search
- ✦ACID transactions; open-source/self-host
- ✦Source-code context and prompt management
- ✦Custom and formatting instructions
- ✦BYOK API integrations (OpenAI, Claude, Gemini, etc.)
- ✦Token-limit tracking
- ✦Code-edit feature with visual diffs and backups
- ✦Local, offline prompt generation
- ✦Side-by-side comparison of 47+ vector database vendors
- ✦Filterable by open source, license, dev language, and index type
- ✦Coverage of hybrid search, geo search, and multi-vector support
- ✦Links to each vendor's own pricing page
- ✦Regularly updated dataset
- →Writing and refactoring production code with AI
- →Planning features before implementation
- →Running agents across multiple IDEs and the CLI
- →Consumer brands monitoring sentiment and share of voice on social video
- →Marketing teams finding and vetting influencer partners
- →CX teams unifying feedback from tickets, chat, surveys and reviews
- →Building real-time reactive apps
- →Backends for AI agents
- →Replacing Firebase or Supabase
- →Full-stack TypeScript development
- →Building context-rich prompts for AI coding
- →Comparing model outputs on the same task
- →Reusing saved prompts across tech stacks
- →Keeping code private during prompt creation
- →Engineering teams selecting a vector database for RAG or search
- →Developers comparing open-source vs. managed vector DB options
- →Researchers evaluating supported index types across vendors