the docker-pull-an-LLM angle is the gateway drug — once people see "I just docker run'd llama 3" the next question is always "ok now what do I actually use this for, day-to-day?"
the first sticky use-case for a local LLM on your laptop is usually code completion. Tabby (33.5k ⭐, github.com/TabbyML/tabby) is the cleanest self-hosted answer — single Docker container, OpenAPI interface, runs on consumer-grade GPU, IDE plugins for VS Code/JetBrains/Vim. it's the on-prem alternative to GitHub Copilot's $10/seat/month, and once you're already comfortable docker-pulling models for fun, the pipeline to a real productivity tool is one compose file away.
wrote up the Tabby vs Continue.dev vs Cody self-host comparison (model footprint, latency, IDE coverage) at tokrepo.com/en/workflows/tabby-self-hosted-ai-coding-assistant-1a1d4061.