lukehan

I am working on a platform to help user to enrich their data by AI. so that AI can understand their Data more, especially for ChatGPT. Also it's easy to host a data and publish a MCP for ChatGPT.

The challenge is how ChatGPT can understand your "query" or say "prompts"? Raw data is not good enough - so I try to use a term called "AI Understanding Score" to measure it: https://senify.ai/ai-understanding-score. I think this index will help user to build more context so that AI can know more and answer with correct result.

This is very early work without every detail considered, really would like to have your feedback and suggestions.

You can have a try with some MCP services here: https://senify.ai/mcp-services

Thanks.

that's true

well, I would like to say the OLAP Cube is just re-rising now. There are 1000+ companies deployed Apache Kylin (OLAP Engine for Big Data) in the past 5 years, for 100+B rows, for 100+ concurrent users...many different use cases are based that technology...it works very well with BI tools and so friendly to analysts who are using such "old fashion" every day over the decade (how hard for them to be Data Scientists?) check more here: http://kylin.apache.org/community/poweredby.html

the document will coming soon, there are many stuff we have moving from internal wiki to kyliy.io now.

The Cube File is stored on HBase now, it could be extend to otehr KV value system.