abalashov

^ This. HN crowd is way too willing to presume the superiority of Anthropic and OpenAI frontier models, or to dismiss the Chinese ones as some distillation parlour trick.

While that is unquestionably going on, these models are legitimately innovative and stand on their own legs.

To be frank, Kimi's a better model...

So you say, but I've done plenty of blind taste tests. The Chinese models are 99% as good for my purposes, and, I think, 99% of applied business programming purposes.

While we don't "token-maxx", our usage of Kimi K3, GLM 5.3, MiMo and Qwen goes up, up, up -- with Kimi K3 dominating. Our usage of Claude Code and OpenAI has been nonexistent for a year and a half...

I hadn't thought of that type of comparison, but it's apt.

I didn't say that. However, the utility delta in the "early days" was infinitely more palpable. By the time GPT-5 or GPT-6 came out, these models were just doing things they did before more accurately, with more context integrity across long-horizon tasks, etc., not doing things they couldn't do before.

This is true of every frontier model. The real agentic coding breakthrough in Claude land was around Opus 4.5 or so, though it's hard to say if that might have just been coeval with the release of the Claude Code harness.

On the Chinese front (my preferred landscape because of the open weight aspect, not because I'm Chinese), Kimi has been perfectly viable since K2.5, DeepSeek since however many major releases ago, and GLM and MiMo and the rest of them too, although they all achieved breakout moments of publicity at different times. There's no denying that every release brings improvements, but if we're going to be honest, they're fairly marginal.

So I'm holding it wrong?

I don't much care about benchmarks. Talk obvious, commonsensical increases in utility to me.

Ah, but he only grew about half an inch from 9 to 10, so the "rate" of "runaway progress" is "accelerating" "exponentially" (wait, how many derivatives is that?), and he will soon EsCapE CoNTaiNmEnT and hack HuggingFace...

You like these evocative words that imbue one with a feeling of "vroom" and "whoosh", like "exponential" and "accelerating", don't you?

But arithmetically speaking, is any of that true? Is model progress truly "accelerating"? How can you compare the delta from GPT-2 to GPT-3 with the delta from GPT-5 to GPT-6 with no sense of irony? And, at the risk of being quite blunt, do you know the meaning of the term "exponential"?

> How can we be so confident that there is anything LLMs fundamentally cannot do? Proving that impossibility seems hard.

This is where applied business programmers part ways with philosophers of mind and cognitive scientists. However, the lack of an inner model of the world is a formidable limitation, while the reliability of purely statistical-inferential processes will never be adequate for some basic building blocks of modernity.

There seems to be two common fallacies in SV technocrats' AI discourse:

(1) A far-reaching tendency to overextrapolate from the low-hanging fruit of the last few years of pretraining progress. GPT-2 to GPT-3 may have been a quantum leap, but GPT-3 to GPT-4 was not, and GPT-4 to 5 even less so.

The party has been kept going by RL and agents, but still, there is indeed a point of diminishing returns, not just relative to available compute but to how much training is possible when the entire intellectual output of humanity, plus a raft of synthetic data, has already been inhaled by the training process.

If one is to internalise the things that are said here on HN with regularity about model progress, and sentences ending with "yet" or "for now", then it would be easy to conclude that my 10 year-old son, who gained 3 inches of height last year, will be tallest structure on the planet by age 17.

(2) Inability to distinguish between technological, computational, and energetic limits of LLM capabilities vs. ontological / conceptual ones. There are some things LLMs cannot do, or at least do well, at any size, at infinite size and with infinite compute, simply due to the very nature of what LLMs are to begin with.

This latter topic receives almost no attention, except maybe from Gary Marcus and Yann LeCun. In that respect, this article is a breath of fresh air, insofar as it highlights that LLMs aren't "AI" at all, as we have traditionally understood the concept.

They really _are_ stochastic parrots. The relevant questions are about how much that matters for some domain or set of applications, not whether they are an emerging alien intelligence with civilisation-threatening capabilities.

The AI bros do keep saying that... next model bro. I really feel the AGI in this one.

Underrated observation, extremely trenchant.

I think a distinction is clearly emerging between docs intended for humans, and docs intended for agents. The latter is where all the Markdown-ridden slop should go; I'm not insulting humans with that.