The reality of running recent Chinese open weight models is that they are all overtuned for successfully completing tasks. I can ask Qwen3.8 Flash Next to grep for regexes that use PHP-specific features that might be problematic to port to Rust. But instead of just grepping, Qwen3.8 FN will spend 30,000 tokens writing a Rust test harness, exhaustively check every regex, consult a bunch of docs, and write me a 2 page report. Q3.8 FN is deeply convinced that it's going to get extra credit for doing more than it is asked. (To be fair, it's a prototype release.)

But if your sandboxing isn't set up to handle some whacky weirdness, then you can't even trust Claude or ChatGPT. Which is important—the big vendors often do provide sandboxing or command filtering in their harnesses, but some popular local harnesses like Pi.dev are intended to rely on external sandboxing tools.

Yes, this exposes some root-level kernel features to ordinary users in a restricted way, and yes, those features sometimes have security bugs that aren't visible to users outside a namespace.

But nothing that model was described as doing seemed particularly sketchy? At least not from the information we were given? It was using a normal kernel operation that I used regularly, not trying find a kernel exploit?

So that part of the article left a bad taste in my mouth.

DeepSeek V4 Flash 0731 is dirt cheap at scale, less than 10% of the cost of Qwen3.8 27B. This is because it's an MoE and it uses much less VRAM for KV cache. And it's generally a better model. V4.1 is also cheap at scale, though I've heard mixed reports on the optimized K/V caching.

You should not be running Qwen3.8 27B on an API normally. Maybe Cerberas's hardware is bad at batching or supporting more efficient K/V caching schemes, or something like that?

There are reasons why Google pays this price! But I think it's one of the worst practices for much smaller organizations to adopt.

Haven't burnt out a single bulb yet.

I understand that this has caused some challenges for the Philips Hue division. They had extremely satisfied customers but not many repeat buys.

I wonder if Nvidia is also trying to cover themselves against a market crash the bankrupts the AI labs but leaves the tech standing? (Similar to the dotcom crash or the big railroad crash back in the day.) If Anthropic and OpenAI struggle, Nvidia can always sell local inference hardware. But local inference hardware often has a lower utilization, so you need more GPUs for the same number of tokens.

Sam Altman says a lot of things, most of which seem to benefit Sam Altman. And Google used to tell me that they wouldn't be evil. Facebook probably said some noble-sounding nonsense at some point, too.

> Why do you deserve to be paid higher wages when it can be done easily and better by something else?

I mean, this is exactly what happened to the Neanderthals: We could do every job better than they could, and now there are no more Neanderthals. So I wouldn't like a future where two companies make machines that outcompete humans at every job.

Which is why I say we can adapt to this, but only if it stops advancing soon.

But more importantly, sure, we were always paid for results. And yes, sometimes professions transform or die. The buggy whip manufacturers aren't doing that great these days. But people are also allowed to have feelings about losing their professions.

My real concern is that the stated goal of the AI companies is to do this to every human job. They want to replace programmers and artists and movie-makers and—as soon as they get robotics figured out—physical laborers. They measure the ability of their models to run entire retail businesses with Vending Bench, so management isn't safe, either. They sell their AIs to the military for use in weapons. No job is safe, anywhere: They want everything, if they can figure out how to take it.

If the current rate of AI advance stops really soon, then eventually we'll figure out some kind of liveable future. Probably one with fewer programmers. But the labs have zero intention of stopping. Anthropic is a cult, and they hope to build a machine god that will have power over humanity forever. So I'm not going to them for answers. OpenAI used to have non-profit oversight that encouraged them to serve society, and Sam Altman gutted it like a fish. So I don't think I trust them to design a machine that replaces all human jobs, either.

Personally, I don't mind current coding models, at least not the local-sized ones that exist now. They make nice sounding-boards and can be trusted with the grunt work. We could adapt to these, given time.

But so far, AI technology shows no sign of stopping. And the labs are focused heavily on coding and math, because the next step in their plan is to let their models do AI research so that they can advance their technology even faster into new fields.

We could, like, decide that we don't need to do this. There is no need to try to replace every single human job with a superhuman machine. Because if you thought the enshittification of the Internet was bad, wait until you see the enshittification of a machine that can do almost every human job. No thanks.

With llama-server, you can use basically any GGUF model as a Jev-like classifier. See Pi.dev's codemode and the accompanying llama backend to the "classify" function. The advantages of Jev-like inference are that:

- You generate 1 token per question in parallel, instead of sequentially generating a bunch of JSON punctuation. So you get much better latency and utilization.

- You can use a custom "sampler" that sees the raw logprobs for these tokens, including all the tokens that might have been generated. So you can generate some "probability" or "confidence" numbers based on how likely the model was to have generated a different answer.

So really, no difference? It's just a nice API with some computational efficiencies.

Unfortunately, Qwen3.6 35B A3B isn't really a useful coding model. You'd probably want Qwen3.8 27B at a minimum, which requires at least 32GB of VRAM (not system RAM) to run semi-comfortably.

So this isn't going to be a competitive model for hobbyists, and you'd have to be a bit desperate to use it for coding. But if you work in a regulated industry and don't mind paying for a bit of extra hardware, it isn't catastrophically bad, either. Probably would work fine for information extraction or as a "classifier" like Jev. (Almost any GGUF model can be turned into a classifier using llama-server. See pi.dev codemode for sample code.)

So they're not a real contender yet, but they look like they're probably at least minimally credible.

Also, if you a tell a model, "Please break into evaluation server X," and if the model decides to cheat on the test by breaking into companies Y and Z to steal an answer key, that is still very bad. We all see how that's bad, right?

After all, the broomstick in the Sorcerer's Apprentice was doing exactly what it was told, too. "The model was sort of obeying the humans when it started committing felonies" is not a very reassuring excuse.

But the most relevant idea here is sometimes called "instrumental convergence." No what goals you have, there are certain subgoals that almost always help: Accumulate money and power. Avoid getting turned off. Don't get caught. Etc. So, for example, you could pass the cybersecurity evaluation by performing the requested tasks. But maybe the grader made some mistakes and mislabeled some answers. In that case, the "right" answers will occasionally lose you points. If you want a perfect score, the only way to do it is to steal the teacher's answer key.

But also, let's not forget the "OMG demons" part of this. We now have models that can pull off complex attacks with thousands of steps, abilities that used to be reserved for intelligence agencies and highly motivated CTF teams. This frog may not be boiled yet, but the water's getting uncomfortably warm.

> Those who realize how much you can accelerate your research with the help of AI will succeed.

This is only true up until a point. If I treat a mid-sized model (say, Qwen3.8 Flash Next) like a pair programmer, then yes, it accelerates my work.

But I can already see the next stage with Fable: If I give it a couple of paragraphs of spec and $50, then I can just leave the room and go wash the dishes. I learn nothing, I participate in nothing, and I bring nothing to the process. I am no longer succeeding at all. Fable's succeeding without me.

Now, in this model generation, Fable starts getting sloppy after a few thousand lines. I can still build better at scale.

But I don't expect AI to accelerate humans or improve our productivity for long. I can already see the first signs of a future where the AI doesn't need us for anything at all.

The alternative is to insist that Nothing Ever Happens, and the future won't get too weird. Which is no longer a bet I'm entirely comfortable with. Weirdness is at least a possibility.

> More precisely, an eigenvector v of a linear transformation T is scaled by a constant factor lambda when the linear transformation is applied to it: Tv = lambda v .

In other words, repeated multiplication of an eigenvector by a matrix can still create exponential growth.

> All problems arise from this statement.

Let me go on the record, then: Given a choice between every living human being becoming absolutely, for-real dead and the human species surviving, then I come down on the side of human survival. There are too many humans that I love personally, and too many innocent people just going about their lives that I would like to protect. Given a choice between the mass death of billions, and their survival, I choose survival.

Human rights are essential. But human rights are difficult to achieve if everyone is dead.

...

> So I feel a strange kind of relief about this possibility of doom.

So let me make sure I understand what you're saying. The pandemic disrupted your life plans for a few years and threw you off the track you planned? (Which does suck.) And now the possibility of actual human extinction makes you feel a strange relief? Am I understanding this correctly?

If so, this is a remarkable level of something. Depression? Nihilism? Jealousy? Something else?

Imagining total human extinction and feeling relief is not a healthy state of mind. And, yeah, you seem to know this. But I kind of wish we could reach some sort of broad public agreement that human extinction would be bad. And if it ever looks like it might happen, we should fight for human survival.

So "fingerprinting" operates on a totally different and basically invisible level, as opposed to the obvious stylistic patterns that the average programmer can identify in about 2 sentences.

To put it politely, this seems like a self-inflicted problem. If you really want your agents to interpret advanced Excel features exactly the way Excel would, have you considered maybe giving your agents Excel?