For models that reveal reasoning traces I've seen their inner nature as a word calculator show up as they spend way too many tokens complaining about the typo (and AI code review bots also seem obsessed with typos to the point where in a mid harness a few too many irrelevant typos means the model fixates on them and doesn't catch other errors). I don't know if they've gotten better at that recently but why bother. Plus there's probably something to the model trying to match the user's style (it is auto complete with many extra steps) resulting in sloppier output if you give it a sloppier prompt.
The edu discount is often $50 or $0 on the lower cost products. E.g. that new M4 iPad Air from earlier this week is also $599 but has a $50 discount on the us-edu store. It's pretty sweet they kept the neo at $100 discount even though edu is one of the primary markets for this.
I'm seeing a ton of comments like that one about how apple is holding ipados back but am I going crazy or wasn't the big story of iPad is last year how many updates it got to make it more of a desktop replacement? Like half the features you mentioned were added last year plus a calculator app right? There was specifically a whole iPad os refresh that was well received as finally massively boosting iPad pro capabilities? Like, very recently?
I mean durability is as or more important than repairability and apple products have a reputation for lasting a long time and holding their value. And getting software support etc. In general I think tech nerds underestimate how much people value durability over repairability and how hard it can be to sell repairability. Ive found that "this product is repairable" can be interpreted by people im trying to convince to buy a framework as "this product will need to be repaired and it's going to be your problem." On the other hand Apple's reputation for durability means that buying a used MacBook to save money is a serious and popular option which is by far the most environmentally friendly option compared to any new device, however repairable. The repairability <-> sustainability relationship isn't as straightforward as people suggest in the real world imo
I haven't been on campus in a few years but even then paper was basically absent on campus. A class where a professor wouldn't allow tablets or laptops to take notes would be an aberration and a PITA. I remember I had to write like a paper check once and I had to physically go buy a pen since neither I nor anyone around me had a regular writing utensil on hand.
The exception was when people were taking orgo or a diagram heavy class. For that semester not everyone would have a tablet and some people would have pens and pencils. Or writing classes that still required a handwritten essay for the final exam
If you live near a community bio lab see if you can join up and take some classes to learn some basic lab techniques. And some sort of intro bio class via mooc/textbook/local college class whatever if you can but community lab is honestly a great place to start if you have one.
The main thing to keep in mind is that all the stuff that involves analogies between software and biology is almost universally a bullshit oversimplification that you can safely ignore. It's just that software is so profitable and there's so much vc money in it that there's a ton of pressure to be like "oh we can program biology like we program computers." We can't - we invented computers but didn't invent biology. Biology is the end result of 4 billion years of unchecked entropy - it's a chaos system, non deterministic in the wildest ways, impossibly complicated, and yet something we are getting astonishingly good at understanding and engineering.
Basically, all the biologists that started companies that were like "we can program biology like we can program computers" are bankrupt now.
On the other hand, the computer scientists that respected the nature of biology and pushed the limits of computing to develop Alphafold - giant models trained on the full complexity of biological data - finally created computer systems that could handle biological systems like protein folding at an extraordinary level of capability. They won a nobel.
Follow up question (Not OP), would alphafold more be used to experiment with an already-defined theory that you have, or could you also make some toy projects (e.g. how people make projects around trading engines).
I'm wondering if I could find a fun weekend project in alphafold just to see what it's like.
I mean - steam deck was a pretty significant inflection point quite recently. Making gaming viable on linux via a popular consumer product is a huge deal and starts to kill one of desktop linux's single biggest barriers to adoption.
If the thing we're measuring is a the ability to write code, visually reason, and handle extrapolating to out of sample prompts, then why shouldn't we evaluate it by asking it to write code to generate a strange image that it wouldn't have seen in its training data?
The difference is that the worker you hire would be a human being and not a large matrix multiplication that had parameters optimized by a a gradient descent process and embeds concepts in a higher dimensional vector space that results in all sorts of weird things like subliminal learning (https://alignment.anthropic.com/2025/subliminal-learning/).
It's not a human intelligence - it's a totally different thing, so why would the same test that you use to evaluate human abilities apply here?
Also more directly the "all sorts of other things" we want llms to be good at often involve writing code/spatial reasoning/world understanding which creating an svg of a pelican riding a bicycle very very directly evaluates so it's not even that surprising?
