Fine-tuning for specific tasks. I'm hoping to see some good examples of that soon - the blog entry mentions things like structured text extraction, so maybe something like "turn this text about an event into an iCal document" might work?

It's intended for finetuning on your actual usecase, as the article shows.

I feel like the blog post, and GP comment, does a good job of explaining how it's built to be a small model easily fine tuned for narrow tasks, rather than used for general tasks out of the box. The latter is guaranteed to hallucinate heavily at this size, that doesn't mean every specific task it's fine tuned to would be. Some examples given were fine tuning it to efficiently and quickly route a query to the right place to actually be handled or tuning it to do sentiment analysis of content.

An easily fine tunable tiny model might actually be one of the better uses of local LLMs I've seen yet. Rather than try to be a small model that's great at everything it's a tiny model you can quickly tune to do one specific thing decently, extremely fast, and locally on pretty much anything.

It's funny. Which is subjective, but if it fits for you, it's arguably more useful than Claude.

Because that's not the job it was designed to do, and you would know by reading the article.

The same as having a goldfish. You can train it to do a trick I guess.

Games where you need NPCs to talk random jiberrish.

Nothing, just like pretty much all models you can run on consumer hardware.

robotic parrots?

An army of troll bots to shift the Overton Window?