The biggest untapped market is pure agentic models that are built for tool calling and non hallucination instead of memorizing facts. You need some world knowledge (as in common sense) to build a useful model, but I don't think perfect recall on general QA is a good use of space when you have web search and structured knowledge in Wikidata or Wolfram Alpha.

Training should focus on tasks that require real intelligence instead of memory. Creative writing is actually good for this if you score it on coherence instead of getting random real life details right. Basic level of coding (simple prompt to code, don't need to one shot complex projects) is also great because writing a small script is more efficient than 20 separate tool calls.

With respect to Wolfram Alpha, it's worth noting that VibeThinker-3B is basically a match for the larger frontier models -- hundreds of times larger -- in the narrow domain of mathematical and logical reasoning problems. It doesn't seem necessary to resort to external models or tools for that, at least in principle.

Agreed. Luckily, this model also scores high in AA non-hallucination, so it knows what it doesn't know -- perfect for situations where it can just tool call a web search.

Yep, exactly. I keep saying that I want the “coding expert” extracted from these multi-T parameter models to run locally on reasonable hardware (large laptops, not servers). Yea, I know there’s no single “coding expert” that you can actually extract in these models, but you get what I mean. Like you said, when I’m coding, I don’t care about world knowledge, and I’m fine with consulting another model when I need that.