first - it's not hard to make. Outside screen size doesn't have to be half of the open screen.
second - main idea is about proportions, if you want to design apps that seamlessly transfer from one screen to another they have to have the same proportions of the sides. This is not a given and depends on the height and width.
If you take iPhone 18 Pro resolution : 2,622 x 1,206 doubling it would make the screen with 2,622 x 2,412 - almost a square screen.
They chose to have the folded phone to have 1.4:1 aspect ratio which is consistent when folded and when unfolded.
What app is going to design to flow nicely with a iPhone Duo but not a iPhone 18? Unless Apple has a 1.4:1 ratio across their entire product line, app designers have to deal with variation.
Also, when you rotate the device, the ratio changes. This is especially common when unfolding a foldable.
Folded and unfolded will both be designed for 2 aspect ratios. 1 for Portrait and 1 for Landscape. Opening the fold and rotating 90 deg will give you the larger version while keeping the aspect ratio
"larger version while keeping the aspect ratio" is of minimal benefit. You still need to present a different layout, because eg. any toolbar is going to be a different fraction of the available area depending on whether the app is on the small outer screen or the large inner screen in portrait orientation. Nobody wants a naive 2x scaling of the phone app on their iPad.
You don't seem to be engaging with the point, which is that the space available for content after subtracting out the space taken up by other UI elements is not going to have the same aspect ratio folded and unfolded. A real UI doesn't actually get this supposed benefit. Only something like a full screen video player might (depends on what kind of keep-out zone you need for the front camera), and maintaining the same aspect ratio folded vs unfolded for video content isn't particularly helpful when the aspect ratio in question isn't popular so you'll have letterboxing regardless.
People keep saying it’s so an app can resize to the full interior screen but none of the marketing photos actually have this. So its hard to say “what a genius masterstroke by Apple.”
Most have the closed screen plus another view. Or the lock screen which is a full photo open and a small section closed.
Im not saying its not possible but it doesn’t seem like that is their intention.
that's not how AI was used in this case. It's more like a professor with assistants.
Professor says the assistants - why don't you dig in this direction, I have a hunch it might produce something valuable. And AI assistant does just that, proving or disproving a hunch. This would take the professor a lot of time if doing by themselves.
Yes, and keyboards also save a lot of time over handwriting. Numerical methods and proof engines save even more time. LLMs are just another tool in the kit.
they threw it on all the millenial math problems (I think there are 6 at this point unsolved, well, 5 now).
And according to them at some point they saw that one was close to being solved, so they pointed all the agents at it.
The same thing happens to humans - at this time there are no simple problems left, so solving the hard ones requires using prior knowledge and attempts at solving things.
Yeah but the one they decided the AI was close to solving may have been so because the researchers' progress on this problem became part of the training data for that AI...
I find the word "castle" can be a little vague, like with Neuschwanstein Castle, which is really more of a palace. I don't think it's worth battling too much about what is or isn't a castle. I don't think you could come up with one set of castles in the world that satisfies everybody.
Neuschwanstein was completed in 1886. Undeniably a very archetypal castle. So much so that of course it was the basis for Disney's Sleeping Beauty castle, which opened at Disneyland in Anaheim in 1955 - at which time the castle it took its inspiration from was not even 70 years old.
Now Disney's Sleeping Beauty castle is itself over 70 years old, making it arguably just as much a historical 'castle' as Neuschwanstein.
Even if no Rust code for it was seen during training, an LLM can trivially transpile SQLite's C codebase to Rust on the fly. For example, I just asked ChatGPT to write John Carmack's famous Fast Inverse Square Root algorithm in Erlang, without searching online or thinking, and it transpiled it immediately (while also extracting the knowledge in the same step). SQLite's semantics/code are stored in the middle layers of an LLM, and the last layers are able to convert it into any representation, as conditioned by the prompt. Cursor's experiment is deeply flawed because they merely extracted the model's compressed, lossy knowledge of SQLite's codebase and then just ran a bunch of tests/fixing rounds to make up for the lossiness. The claim that the agents built it from scratch is false.
Then you would expect the implementation to be structured the same as SQLite, and having glanced at the result, it looks like at least some things aren't. For example, it seems to use an operator-tree executor rather than SQLite's bytecode interpreter.
The knowledge is lossy, and code generation itself is non-deterministic (temperature), so the operator-tree executor must be interference from other DB implementations, because it's uncommon to have a bytecode interpreter
The point is that the model has the source code in its weights. Therefore it's misleading to focus on being "only given the documentation", when it could produce a SQLite implementation in rust with no documentation at all. The wording is designed to create a marketing impression that it can do spec-driven development for a novel project, but this example doesn't actually support that.
It's like a diet system advertising how much weight a spokesperson lost on their system, without disclosing that they also went on Ozempic at the same time.
Why tho? The whole point of agentic is that it takes several passes to get to a result. If step a) is a non-idiomatic transpile, but step b) c) and ... z) make it idiomatic and performant and efficient, why look at step a)? That was just the beginning. The idea with "agents" and "swarms" and dynamic this and that is that you only look at the last step.
This strictly follows the early agentic usage, where people were complaining of code full of errors and such. But having the loop do plan-write-verify is the thing that got us from that state to today. And it works. If you look at the code and it has errors (linting, etc) then your loop is not properly setup. You should only be called upon when all that can and should pass does. Your role is "direction/taste/human-common-sense-at-a-glance".
Because the point of the test is to see what the agentic swarm approach is capable of. If your test assumes that a version of the thing you are trying to build is available in the training data, or can't determine if it matters whether the thing you're building is in the training data, then it's not a very useful test. Most of the time when you consider agentic approaches, you're not trying to rebuild something the agents involved are trained on.
"OpenAI will launch a portable, screen-free smart speaker as its first consumer hardware product, Bloomberg News has reported, days after Apple sued the AI start-up and two former employees of the iPhone maker for trade-secret theft." [1]
> At least before it would listen to instructions like this.
Would it actually follow them? IME LLMs are incapable of estimating the length of their own output, the total length of the current context, etc. They just make stuff up unless they have external tools that can inspect those things for them.
That was the case for early models (Llama etc), but they got much better since then. Not perfect, but good enough.
This is from Ministral 3 14B, a 2025 model without reasoning, that you can run on your PC:
> Write a Haiku involving HackerNews, and the capability of large language models like you to reply in an exact number of words or syllables.
Silicon whispers,
exact words in code’s embrace—
Haiku blooms anew.
Across multiple tries it got it wrong a couple times (by ~2 syllables). But syllables are extra tricky (because of how LLMs use tokens) and the point is that for things like "summarize in 5 bullet points" you will mostly get 5 bullet points, maybe 6, but not 10 or 20, and no need for a tool that count bullet points.
they are paying for marketshare/customer base. Cursor has a good chunk of it.
xAI overbuilt their data centers - they can't find paying customers for them, that's the reason they made deals with other companies like Google to use their own datacenters.
Cursor has the opposite problem of not having enough capacity. So this works well for them together.
Weather it's worth it - if you beleive that AI will solve every problem then having a piece of the pie early on might be worth it.
Remember how when google bought youtube for 1.65 billions people thought they are crazy? Or when facebook bought instagram.
60B is a crazy number but might be worth it for someone fighting for world dominance :)
xai is on the line to delivery capacity they already sold to Google and most analysts think they are 50/50 on actually meeting it.
the only proof they have capacity is that musk claims all the money they are burning is going to datacenters and gpu (mostly because if he put it on anything else the lie would be obvious)
Or are they paying for talent? It seems like xAI is sorely lacking in talent, most likely due to the CEO and folks' aversion to him. By throwing around some SpaceX monopoly money he can trap some talent with retention clauses and try to invigorate his failed AI business.
second - main idea is about proportions, if you want to design apps that seamlessly transfer from one screen to another they have to have the same proportions of the sides. This is not a given and depends on the height and width.
If you take iPhone 18 Pro resolution : 2,622 x 1,206 doubling it would make the screen with 2,622 x 2,412 - almost a square screen.
They chose to have the folded phone to have 1.4:1 aspect ratio which is consistent when folded and when unfolded.
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