FWIW, people were saying "ASICs will kill CUDA demand!" since the crypto mining boom. Then a few months later, CUDA found another niche application in LLM applications.

With the mounting demand for robotics, surveillance and autonomous weapons, I don't see how Nvidia couldn't keep their spot. They have their pick of the litter with hundreds of market segments, and unlike the rest of FAANG they're not afraid to branch out.

To my surprise I couldn't find any proper explainers of the term in a quick search, despite grokking it after seeing it in various contexts on Twitter, but Fable 5 offered a useful analogy: "A student who memorized worked solutions and one who understands the subject score the same on the test; you can only tell them apart by asking a question the test didn't. Real-world use is nothing but those questions, which is why a single AA number feels right and wrong at the same time."

In other words, big model smell is related to the underlying ability to "understand" when tasks are underspecified or out-of-distribution. This ability can be mimicked to parity by smaller, distilled models according to the density of the training data for particular tasks, but neural scaling laws still hold for generalized reasoning ability.

More recently with these smaller models, there's a separate but related "RL-fried" phenomenon, where they rely on CoT to "grind toward a checkable answer even in contexts (open dialogue, taste, ambiguity) where there is no checkable answer, and you get the tell: over-hedged, over-structured, relentlessly on-task, deaf to the subtext."

There are some other insights and caveats in the (short) conversation that I feel you may appreciate reading. [1]

[0] https://x.com/aidan_mclau/status/1807843014104211855 [1] https://claude.ai/share/d511a348-7c36-432f-a6d5-9deab2802615

For implementation tasks, where I have the problem already defined and researched, or just simple task, I'd definitely use something like Luna xhigh or max. If the task is vague, or involves planning, I'd rather use Sol medium, even though it's theoretically worse on benchmarks.

It's at least close (even if not better) from the Ox Alpha runs. For the price it's definitely great.

│ https://openrouter.ai/api/v1/chat/completions model: stealth/ox-alpha auth: OPENROUTER_API_KEY status: 404 Not Found response: {"error":{"message":"Thank you for participating in the Stealth Ox Alpha testing period. This model was ZAI's GLM-5.3 Flash.

│ Use it now: https://openrouter.ai/z-ai/glm-5.3-flash","code":404},"user_...":"}

* me raises hand.-

Think z code gives a token bonus though

Have been using it as my primary harness for personal work for I'd say 6 months. I recommend everyone create their own harness at least to learn. There are a lot of practical benefits.

edit: nevermind. it is there in the artifical analysis scatter plot, but is greyed-out.

MUCH more interesting is that in that chart, their cost is WAY off. The actual chart shows GLM 5.3 Flash at $0.09, but their chart shows $0.045...