Yet again, Claude is down. Yet again, my local models just keep working. A case study in why the complexity of self-hosted AI is worth it.

Part two of the quest to deliver AI applications on-demand from Kubernetes. In this one, we’ll deploy a typical AI application, and configure it to scale up (and down to zero) on demand.

Extending my Kubernetes cluster with a GPU/CUDA node to deliver on-demand AI applications

Notes on integrating a local LLM with JetBrains IDEA IDEs

Using a genuine multi-modal AI model to generate photo captions gives a huge leap in quality over my previous efforts.

Playing with contemporary machine-learning models can demand hardware with a pretty hefty pricetag. Modal lets you do it in the cloud with a much more reasonable pricing model than the big Cloud Compute providers.

A second attempt at using AI to generate some captions for my photographs

Photographs taken in Bucharest’s Dimitrie Brândză Botanical Garden, captioned by AI

I don’t know if you’ve noticed, but search engines got a lot less useful lately…