ainch

6 ms·

1.
by ainch 6d ago
To my understanding, the problem was never about the real world really. Navier Stokes approximates a fluid (which is made of discrete particles) as a continuous volume. The point of showing that you can achieve unbounded increase in velocit
2.
by ainch 7d ago
It was my favourite part of the original R1 paper - they had a section on other reasoning approaches that they had tried, which people had speculated o1 used, (like MCTS and Process Reward Models).
3.
by ainch 7d ago
Self-driving cars also operate at a far larger scale than individual human drivers. Over 300 people died in Boeing 737 crashes, but the entire aviation industry has not been shut down as a result.
4.
by ainch 13d ago
Google also bought capacity from xAI, and OpenAI have a deal to use Google compute which may be how things propagated? I still don't get why chatgpt.com would show a 404 because of an AI datacentre outage though
5.
by ainch 13d ago
As someone who could be tempted by Julia but isn't involved in the community this was a very helpful read, thank you for sharing.
6.
by ainch 14d ago
Out of curiosity, what would you like to hear him saying about non-LLM AI?
7.
by ainch 17d ago
A great read - as with all of Sander's diffusion posts.
8.
by ainch 22d ago
What do you mean by poisoning attacks - stuff like Nightshade or Glaze? I was under the impression that those have largely failed to achieve their goals.
9.
by ainch 22d ago
"Model collapse" is often overstated, as this paper demonstrates: https://arxiv.org/pdf/2404.01413 The original model collapse paper assumes you train networks on 100% synthetic data produced by the previous
10.
by ainch 28d ago
What problems do you run into for maths with Python? For linear algebra and ML with Jax/Numpy I find it quite readable.
11.
by ainch 28d ago
I think the point is that, from a compiler's perspective, it's not obvious how much you should be allowed to optimise code at the cost of changing the outcomes of floating points maths - do you allow 1e-10, or 1e-6, or 1e-4 level
12.
by ainch 1mo ago
Does it? Mojo was announced 6 months after ChatGPT was released.
13.
by ainch 1mo ago
I wouldn't say there was a pivot, AI development has explicitly been the goal since launch.
14.
by ainch 1mo ago
Quite - you still have to do the work yourself. I think LLMs are best placed to act as an eager tutor that doesn't mind discussing a topic ad nauseam until you're certain you understand it.
15.
by ainch 2mo ago
I'm not sure I agree about retyping calculus solutions. I often find that writing out a proof or derivation forces me to engage with some minor detail that I hadn't fully appreciated beforehand. That usually raises productive ques
16.
by ainch 2mo ago
> It would be foolish to deny the effectiveness of image recognizers based on generalized adversarial networks (GANs), the key neural network technology underlying LLMs I could be misreading this, but I hope the author doesn't think
17.
by ainch 2mo ago
I'm sure you could select for shorter proofs, but then that might be confounding in its own way. I think it's a general problem for LLMs that taste is both subjective and hard to pin down to a single metric. There's a reason
18.
by ainch 2mo ago
I'm not sure that elegance will be so easy to train for, the same way that writing skill has plateaued (or arguably declined) since earlier models. "Have you solved the problem" is verifiable, but questions of taste are harde
19.
by ainch 2mo ago
Perhaps amongst coders, but I doubt most users are interacting with TUIs - my retired mother uses ChatGPT most days, but I suspect it'll be a while before she downloads Claude Code.
20.
by ainch 2mo ago
The chronology in the article seems to suggest that they moved (or were moved) off of AlphaFold earlier this year, prior to quitting.
21.
by ainch 2mo ago
John Jumper moving to work on LLM coding capabilities does seem quite shortsighted - and little surprise perhaps that he then quit to work on AI for science again at Anthropic.
22.
Google DeepMind dismantles AlphaFold team(ft.com)
52 points by ainch 2mo ago|20 comments
23.
by ainch 2mo ago
It's heartening to see that Delayed Gratification has successfully continued their work for so long. I used to work at Tortoise (who now own/operate The Observer) and our motto was 'Slow News'. We operated on a pretty tr
24.
by ainch 2mo ago
I think this idea is exactly right. Many ML papers that include pseudocode basically just write out a minimal PyTorch training loop - down to the loss.backward(). It has, along with Python, become something of a linga franca for research in
25.
by ainch 2mo ago
Some data is so sensitive it likely has to stay on premises though.
26.
by ainch 2mo ago
It is cheaper than GPT-5.6 and Anthropic's models. But then on coding agents specifically (if we take artificial analysis, at least - I'm sure there's a better meta-review you could do) Grok-4.5 scores better and is 20% cheap
27.
by ainch 2mo ago
No I think you're right that the amount of compute spent on office work is lower than coding - although I don't have any sense for the right share. The best source I could find was an OpenAI report [1] which mentions that ~64% of
28.
by ainch 2mo ago
Agreed, Kimi is cheaper for coding - I say that explicitly in the post too. However I'd have to disagree with you on the "office task" front. General office work is one of the big frontiers the labs are pushing on, and it&#x2
29.
by ainch 2mo ago
That's a very fair critique. I don't mean to imply that Kimi is not at all cheaper than U.S frontier models. I more wrote this because I believe - since Chinese LLMs entered the public consciousness via DeepSeek R1, which was genu
30.
Kimi K3 is not cheap(alexinch.com)
23 points by ainch 2mo ago|25 comments

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