AI & Data Systems Artisan

An earlier post covered building a local LLM stack with Ollama, Open WebUI, and Continue.

LangChain has quietly built the most capable stack for production AI agents.

Retrieval Augmented Generation (RAG) has revolutionized how Large Language Models (LLMs) access and utilize external knowledge, moving beyond static training data to deliver more accurate, relevant, and up-to-date responses.

In the rapidly evolving world of Large Language Models (LLMs), the ability to run these powerful tools locally on your hardware is likely appealing.

The concept of a data lakehouse is gaining serious traction, and for good reason!

Popular data processing platforms offer users the ability to inject an external process into the data processing pipeline.

In an earlier post, we looked at how to create a Docker image for SciDB.

One of the strengths of SciDB over other database management systems is its extensibility.1 SciDB allows the user to add new data types, functions, and operators.

In part 1 and part 2 of this multi-part post, we looked a how to load data from multiple files while capturing information present in the file name.

SciDB has extensive documentation but there is no official tutorial or getting started guide.