The Container Orchestration Complexity Trap - Finding the balance between simplicity and scale

Do You Really Need an AI Platform or Can Open Source Get You 90% There? - When open models are enough

R Community Rejoice: Coding Assistants for R Users - What Claude Code gets right (and wrong) as an R coding assistant

Claude Code as an Agentic Workflow Library - From messy ideas to working agents in a single session

DGX Spark Series (Part 2): Configuring the server with Ansible - When AI actually helps write infrastructure code

DGX Spark Series (Part 1): Setting up the DGX Spark - Serious AI compute, no data center required

How to get a real prediction from public data with better prompts Before the Seattle Seahawks  and New England Patriots  take the field on Sunday, a lot of fans are going to do the same thing: Open a large language model and type, “Who’s going to win the Super Bowl?” I get it. It’s fast. It’s fun. It also tends to produce information that’s basically the sports equivalent of a fortune cookie. I’m doing a little more work than that, using only publicly available information th

Why We Don't Just Trust the Model The case for grounding, retrieval, and verification in enterprise AI

Can Small and Medium-Sized Organizations Compete in AI? How nimble teams create real value with the resources they already have

Welcome back to the Lander Analytics Blog - A place where data-driven exploration meets practical LLM solutions