Why You Need to Follow Up After Your Data Science Project

ML systems, production & scaling, execution & collaboration, building for users, conference etiquette.

Look at and label your data, build and evaluate your LLM-evaluator, and optimize it against your labels.

Being a human judge at the Weights & Biases LLM-as-a-Judge Hackathon

FastAPI, FastHTML, Next.js, SvelteKit, and thoughts on how coding assistants influence builders' choices.

Use cases, techniques, alignment, finetuning, and critiques against LLM-evaluators.