My Lindy alarm has gone off!
Readit Newsandrewgleave commented on The zipper is getting its first major upgrade in 100 years wired.com/story/the-zippe... · Posted by u/bookofjoe
Looks like MLX is not a supported backend in Ollama so the numbers for the Mac could be significantly higher in some cases.
It would be interesting to swap out Ollama for LM Studio and use their built-in MLX support and see the difference.
andrewgleave commented on Legends of the games industry: Roger Dean spillhistorie.no/2025/10/... · Posted by u/thelok
For any stamp collectors here, the Isle of Man Post Office [1] has just issued an official set of 6 Roger Dean and Rick Wakeman stamps [2]:
[1] https://iomstamps.com/collections/wakeman [2] https://www.bbc.co.uk/news/articles/clyqe679gqno
That was exactly what UML wanted to do, and it almost never worked out in practice.
Seems to be just a rehashing of the same idea but instead of XML, and diagrams, it's now some free-text to be interpreted by LLMs, so much less deterministic and will probably fail just like UML failed.
People also tend to forget about Peter Naur's take on "Programming as Theory Building" [0], the program is, in itself, the theory of what's implemented. A spec cannot replace that.
Theory building is the secret sauce, and all variants of "this is how to use AI effectively" I've seen are inferior to the epistemologically sound theory Naur outlines in his paper.
There's some sort of fundamental category mistake going on with thinking like this.
Most of the items in this list fall prey to it, but it is maybe best exemplified by this one:
> A writing app that lets you “request a critique” from a bunch of famous writers. What would Hemingway say about this blog post? What did he find confusing? What did he like?
Any app that ever claimed to tell you what "Hemingway would say about this blog post" would evidently be lying — it'd be giving you what that specific AI model generates in response to such a prompt. 100 models would give you 100 answers, and none of them could claim to actually "say what Hemingway would've said". It's not as if Hemingway's entire personality and outlooks are losslessly encoded into the few hundreds of thousands of words of writing/speech transcripts we have from him, and can be reconstructed by a sufficiently beefy LLM.
So in effect it becomes an exercise of "can you fool the human into thinking this is a plausible thing Hemingway would've said".
The reason why you would care to hear Hemingway's thought on your writing, or Steve Jobs' thoughts on your UI design, is precisely because they are the flesh-and-bone, embodied versions of themselves. Anything else is like trying to eat a picture of a sandwich to satisfy your hunger.
There's something unsettling that so many people cannot seem to cut clearly through this illusion.
Feynman said, "The first principle is that you must not fool yourself - and you are the easiest person to fool" when talking about science, but it also applies to the properties of LLM output.
Not cosmological but yesterday Apple released an interesting protein folding model with 3B param transformer-based arch which runs on M-series hardware and is competitive with state-of-the art models. [1] Code [2]
[1] https://arxiv.org/pdf/2509.18480 [2] https://github.com/apple/ml-simplefold
The best code I've written with an LLM has been where I architect it, I guide the LLM through the scaffolding and initial proofs of different components, and then I guide it through adding features. Along the way it makes mistakes and I guide it through fixing them. Then when it is slow, I profile and guide it through optimizations.
So in the end, it's code that I know very, very well. I could have written it but it would have taken me about 3x longer when all is said and done. Maybe longer. There are usually parts that have difficult functions but the inputs and outputs of those functions are testable so it doesn't matter so much that you know every detail of the implementation, as long as it is validated.
This is just not junior stuff.
Yes. Juniors have a lack of knowledge about how to build coherent mental models of problems whose solution will ultimately be implemented in code, whereas seasoned engineers do.
Seniors can make this explicit to models and use them to automate "the code they would have written," whereas a junior doesn’t know what they would have written nor how they would have solved it absent a LLM.
Same applies to all fields: LLMs can be either huge leverage on top of existing knowledge or a crutch for a lack of understanding.
andrewgleave commented on EU court rules nuclear energy is clean energy weplanet.org/post/eu-cour... · Posted by u/mpweiher
For anyone interested in the history of Sellafield and its role in reprocessing, "Britain's Nuclear Secrets: Inside Sellafield" on BBC 4 at the moment is worth a watch. Presented by Jim Al-Khalili.
Reminds me of Brett Victor's demo of projected AR turbulence around a toy car at Dynamicland. Only a short clip, but you get the idea: https://youtu.be/5Q9r-AEzRMA?t=47
andrewgleave commented on Anthropic raises $13B Series F anthropic.com/news/anthro... · Posted by u/meetpateltech
The compute moat is getting absolutely insane. We're basically at the point where you need a small country's GDP just to stay in the game for one more generation of models.
What gets me is that this isn't even a software moat anymore - it's literally just whoever can get their hands on enough GPUs and power infrastructure. TSMC and the power companies are the real kingmakers here. You can have all the talent in the world but if you can't get 100k H100s and a dedicated power plant, you're out.
Wonder how much of this $13B is just prepaying for compute vs actual opex. If it's mostly compute, we're watching something weird happen - like the privatization of Manhattan Project-scale infrastructure. Except instead of enriching uranium we're computing gradient descents lol
The wildest part is we might look back at this as cheap. GPT-4 training was what, $100M? GPT-5/Opus-4 class probably $1B+? At this rate GPT-7 will need its own sovereign wealth fund
> “There's kind of like two different ways you could describe what's happening in the model business right now. So, let's say in 2023, you train a model that costs 100 million dollars.
>
> And then you deploy it in 2024, and it makes $200 million of revenue. Meanwhile, because of the scaling laws, in 2024, you also train a model that costs a billion dollars. And then in 2025, you get $2 billion of revenue from that $1 billion, and you spend $10 billion to train the model.
>
> So, if you look in a conventional way at the profit and loss of the company, you've lost $100 million the first year, you've lost $800 million the second year, and you've lost $8 billion in the third year. So, it looks like it's getting worse and worse. If you consider each model to be a company, the model that was trained in 2023 was profitable.”
> ...
>
> “So, if every model was a company, the model is actually, in this example, is actually profitable. What's going on is that at the same time as you're reaping the benefits from one company, you're founding another company that's like much more expensive and requires much more upfront R&D investment. And so, the way that it's going to shake out is this will keep going up until the numbers go very large, the models can't get larger, and then it will be a large, very profitable business, or at some point, the models will stop getting better.
>
> The march to AGI will be halted for some reason, and then perhaps it will be some overhang, so there will be a one-time, oh man, we spent a lot of money and we didn't get anything for it, and then the business returns to whatever scale it was at.”
> ...
>
> “The only relevant questions are, at how large a scale do we reach equilibrium, and is there ever an overshoot?”
From Dario’s interview on Cheeky Pint: https://podcasts.apple.com/gb/podcast/cheeky-pint/id18210553...