Dingo MCP Server

# 文案一 🚀 RELEASE: The most comprehensive AI data quality metrics documentation ever assembled! 📊 50+ evaluation metrics 🎓 Academic-backed (RedPajama, CLIP, NIMA...) ⚡ Rule-based + LLM evaluation 📖 Metrics: https://github.com/MigoXLab/dingo/blob/dev/docs/metrics.md ⭐ GitHub: https://github.com/MigoXLab/dingo What's your go-to data quality metric? 🤔 --- # 文案二 🔥 Dingo MCP Server is LIVE! Integrate AI data quality evaluation directly into Cursor IDE ⚡ Real-time evaluation 🛠️ Seamless workflow 📊 Instant feedback ⭐ GitHub: https://github.com/MigoXLab/dingo #MCP #Cursor #AITools #DataQuality --- # 文案三 - Dingo 1.9.0 Release ## Tweet 1 - Main Release 🚀 Dingo 1.9.0 is HERE! ✨ RAG hallucination detection ⚙️ Revamped config system 📚 DeepWiki document Q&A AI data quality just got smarter 🧠 ⭐ https://github.com/MigoXLab/dingo #DataQuality #RAG #AI --- ## Tweet 2 - RAG Focus 🎯 RAG hallucination detection hits 94.6% accuracy! Smart retrieval + context validation = goodbye AI hallucinations ⭐ https://github.com/MigoXLab/dingo #RAG #AI #HallucinationDetection --- ## Tweet 3 - DeepWiki 📚 DeepWiki turns docs into smart assistants! Multi-language support + 1s response time 📖 Try: https://deepwiki.com/MigoXLab/dingo ⭐ GitHub: https://github.com/MigoXLab/dingo #Documentation #AI --- # 文案四 - Dingo × ArtiMuse Integration Ready to upgrade from "gut feeling" to "standardized selection"? 🔗 Dingo: github.com/MigoXLab/dingo 🔗 ArtiMuse: github.com/thunderbolt215/ArtiMuse Perfect for content QA, brand guidelines, or creative iteration! #AI #ImageGeneration #QualityAssurance ## Standalone Tweet Option 🚀 Built an automated image quality pipeline: nano banana generates → ArtiMuse evaluates (8 aesthetic dimensions) → Dingo orchestrates everything Result: 75% pass rate, detailed feedback on composition/lighting/originality Open source: github.com/MigoXLab/dingo