#graphdb — Public Fediverse posts

Let me use this time to write down what I'm building. Not what's in my git repos. What's in my head.

The system is distributed. And distribution is optional. Each node has their own state, their own runtime, their own persistence. All one thing.

Each node in the system is a content-addressed Merkle Tree. Not SHA. Coincidence. A spectral coincidence hash. 5 dimensions. (1 more than spacetime.)

A spectral coincidence hash describes the structure of the data. You can do math on that. Spectral graph analysis. The world becomes navigatable. Because everything has an address.

Each computation. Each inference. Each failure. Each partial success. Each time information was lost. All of it enters the graph. All of it local. All of it distributed if needed. (For the BEAM engineers: it's an mnesia backend.)

A system that doesn't only know what it knows and what it doesn't. A system that knows how it got there. And AI that lives inside that knowledge.

That's what I'm building.

#SovereignTechFellowship grant application open. Release on the horizon. Wish me luck!

#OpenSource #EUTech #AI #Erlang #Fortan #Rust #GraphDB

Knowledge graphs are useful representations for knowledge bases, #pkm, #AImemory systems, #GraphRAG, intelligent tutoring systems, etc., and usually implemented in graph databases. LadybugDB, a fork of the discontinued Kuzu, is a lightweight embedded (like SQLite) graph database: https://github.com/LadybugDB/ladybug
Sample applications in development: https://github.com/inventivepotter/dotmd & https://github.com/tejzpr/Smriti-MCP
See also Grafeo: https://github.com/GrafeoDB/grafeo
#AIEd #AIEngineering #KnowledgeGraph #GraphDB #graphdatabase

Knowledge graphs are useful representations for knowledge bases, #pkm, #AImemory systems, #GraphRAG, intelligent tutoring systems, etc., and usually implemented in graph databases. LadybugDB, a fork of the discontinued Kuzu, is a lightweight embedded (like SQLite) graph database: https://github.com/LadybugDB/ladybug
Sample applications in development: https://github.com/inventivepotter/dotmd & https://github.com/tejzpr/Smriti-MCP
See also Grafeo: https://github.com/GrafeoDB/grafeo
#AIEd #AIEngineering #KnowledgeGraph #GraphDB #graphdatabase

Knowledge graphs are useful representations for knowledge bases, #pkm, #AImemory systems, #GraphRAG, intelligent tutoring systems, etc., and usually implemented in graph databases. LadybugDB, a fork of the discontinued Kuzu, is a lightweight embedded (like SQLite) graph database: https://github.com/LadybugDB/ladybug
Sample applications in development: https://github.com/inventivepotter/dotmd & https://github.com/tejzpr/Smriti-MCP
See also Grafeo: https://github.com/GrafeoDB/grafeo
#AIEd #AIEngineering #KnowledgeGraph #GraphDB #graphdatabase

Knowledge graphs are useful representations for knowledge bases, #pkm, #AImemory systems, #GraphRAG, intelligent tutoring systems, etc., and usually implemented in graph databases. LadybugDB, a fork of the discontinued Kuzu, is a lightweight embedded (like SQLite) graph database: https://github.com/LadybugDB/ladybug
Sample applications in development: https://github.com/inventivepotter/dotmd & https://github.com/tejzpr/Smriti-MCP
See also Grafeo: https://github.com/GrafeoDB/grafeo
#AIEd #AIEngineering #KnowledgeGraph #GraphDB #graphdatabase

Knowledge graphs are useful representations for knowledge bases, #pkm, #AImemory systems, #GraphRAG, intelligent tutoring systems, etc., and usually implemented in graph databases. LadybugDB, a fork of the discontinued Kuzu, is a lightweight embedded (like SQLite) graph database: https://github.com/LadybugDB/ladybug
Sample applications in development: https://github.com/inventivepotter/dotmd & https://github.com/tejzpr/Smriti-MCP
See also Grafeo: https://github.com/GrafeoDB/grafeo
#AIEd #AIEngineering #KnowledgeGraph #GraphDB #graphdatabase

Also if you are into #rdf and #knowledgegraph you have heard of tinker pop gremlin 👹 then the following pipeline might remind you some good souvenir:

´´´
(let ((g (make-graph gremlin-n gremlin-e 10 12345)))
(display "Graph: ")
(display gremlin-n)
(display " vertices, ")
(display gremlin-e)
(display " edges/vertex, 10 groups")
(newline)
(let ((tri (time
(traverse g
(V)
(as a)
(out)
(as b)
(where (same-group? g a b))
(out)
(as c)
(where (same-group? g a c))
(where (edge? g c a))
(count)))))
(display tri)
(display " triangles")
(newline)))
´´´

ref: https://github.com/amirouche/seed/blob/seed3/src/src/seed3/benchmarks/gremlin-pipeline.seed3

#scheme #graphdb

https://blog.martinscloud.com/graphdb-uise-docker-compose-for-graphdb/ #docker #compose #docker_compose #docker_container #graphdb