I stopped reading when the author became overwhelmed by their compulsion to blame everything in the universe to the current US president.

And they're stautorily exempted from a whole boatload of privacy laws, like HIPAA.

But the basic single-NN frontier capability has been pretty stationary since Opus 4.8. Kimi K3 is almost as good as that with open weights, which has the frontier labs terrified.

The only big thing on the horizon is if we can get diffusion models working reliably; that would be a big step forward. Inception's Mercury is AFAICT the leader here. It's stupifyingly fast but has obedience/hallucination problems that the autoregressives solved ~2 years ago. So it's not ready yet but improving.

Also, FFS why is Grok the only model that knows how to do parallel tool calls? Such a useful ability and nobody else trains it in. Or if they do it just doesn't work.

It's his rejection of #1 that makes me sad.

This subjective attitude turns mathematics into nothing more than number-poetry.

That would reduce mathematics to something very pathetic.

Focus instead on attribution. Yes, OpenAI took the last tiny step in the process of solving this problem (= proving it). But it cannot attribute credit to all the mathematicians whose chat logs from the past few months were fed into its training data. Unlike a human, it can't even remember where it learned things from! For many theorems, I can still recall which exposition was the one that "sank in" for me (often not the first one!) a decade after grad school.

In my mind, this makes current LLMs unfit to deserve any credit at all -- they cannot give credit to others, so they and their owners deserve no credit themselves. OpenAI's LLM took the last tiny step, but not any of the important ones.

Just compare with the comments on https://news.ycombinator.com/item?id=49639408

I fear that AI is going to cause ossifying secrecy in many fields, much like what happened semiconductor design the past 10-15 years.

This really need to be a top-level story on HN..

I asked whether the model had been trained on, or had access to, our sessions in Codex, into which we had been putting all our drafts for the whole of this project. I was told the model did not look up user data. I asked again, about training, and I did not get an answer.

This whole episode is more horrific than "AI is eating math". We now have a clear and economically damaging (or at least career damaging) example of the "training on customer tokens" problem.

We can't ignore this problem any longer.

In fact the whole situation is very much analogous to Kernel Lockdown, which is controversial.

With signed bootcode you can give a reason for not letting the userspace modify the SMM code. But when all this stuff was designed there were no bootcode signatures.

(The fact that there is such a thing as a higher privilege level than root is what annoys people).

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And if you catch somebody submitting code they can't explain, you don't know them anymore.

It's really simple. Open source has always had a social layer. Github culture tries to eliminate it, and that's the source of this vulnerability.

Linux's LLM policy allows AI-generated code because the project was never vulnerable to this DoS attack in the first place. They kept the social defense they've had for decades.

The killzone here is Github culture, where people decided it was normal to accept code from anonymous randoms with anime avatars. They're doomed.

Also, if you eliminate all the aging-related causes of death the average lifespan is still only like 1000 years or something. Nobody's getting eternity. And yeah, for the chance to make a big enough change of the right kind to society I would indeed give up a 1000-year lifespan. My memes have fully subjugated my genes, and I'm at peace with that.