This is some bizarre victim inversion. The providers of closed models are the ones who are trying to use regulation to stop their open model competition, not the other way around.
Now, what if you order a construction crew for building your house? What if you also hire an architect to design the plan? And an overseer who manages everything?
Surely at some point you would stop saying "I built this house", even if you ordered and financed it?
Sure, but "10x faster, but only applies on small greenfield, throwaway projects" is a major caveat. In fact, there's a good chance this doesn't disprove the original blog post, you could be way faster on small projects but slower on 'real' projects.
>Also give it a chance - as you said "good" models have only been available very recently and you wouldn't expect everyone to start using them instantly.
But I'm not expecting everyone to have built something like that, but surely among millions of users someone should have, especially the people proclaiming insane productivity gains? There are no super impressive open source projects done using AI and all the companies boasting about how all their code is AI written now don't show much improvement either.
Opus 4.5/4.6 are what many people consider the first 'good' models and it's from last year/start of this year.
But fine, let's say everything before gpt 5.5 was unusable crap. Then there should still be projects that would normally have previously taken ~2 years done in just two months. Where are they?
>I just did a ~6 month project in ~2 weeks using a frontier model.
Claims like this are hard for me to take seriously because 'good' models have been available since the start of the year. So, if they really 10x one's productivity, then people should be able to have gotten done 5 years worth of work since then, but I've never actually seen anybody show any project like this.
This is a very strange mindset. Even if you want to treat everything as sort of billable hours this doesn't really make sense because the average boss's boss's isn't paid anywhere near 144x. If a SWE spends 100 hours to save their boss's boss one hour, they're wasting a ton of money.
It's when a woodworker, musician or painter completely outsources their work and just marks what's wrong, sending those parts back. Yes, the final art piece might be the same, but the artist definitely uses less of their "soul".
Shared memory of the past meant reserving a part of the memory for the GPU, which could then not be used or accessed by the CPU. If the CPU wanted to access something, it had to copy it from the GPU's section of the memory to its own. Unified memory means both just fully share the same memory.
The quill and ink at least communicates that it's about writing. The new one is so abstract that when I first looked at it I had no idea what I was even looking at, it certainly doesn't communicate "this is like word" to me. Without comparison to the previous icon, how many people do you think would understand that the bottom line is intended to be a stroke drawn by the pen?
The key thing is that you teach multiplication tables in a structured, incremental manner. Yes, it's just rote memorization, but the structure makes it way easier. You don't just dump all tables on the student at once and start quizzing them until they get it.
Imo not being able to select a subset of intervals to train heavily limits how useful this is.
This is the fault of sloppy language. In Lean, _proofs_ (equivalent to functions) and _proof objects/certificates_ (values) need to be distinguished. You can't compute proofs, only proof objects. In the above quote, replace "proof" with "certificate" and you'll see that it's a perfectly valid (if trivial - it essentially just applies a lemma) proof.
Caveat: Coercions exist in Lean, so subtypes actually can be used like the supertype, similar to other languages. This is done via essentially adding an implicit casting operation when such a usage is encountered.