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.
I think the fact that all (good) LLM datasets are full with licensed/pirated material means we'll never really see a decent open source model under the strict definition. Open weight + open source code is really the best we're going to get, so I'm fine with it coopting the term open source even if it doesn't fully apply.
I've admittedly based that on what a FSF advocate told me and googling it seemed to support it. If you have some source that indicates this is wrong/biased, feel free to link it.
Anyway, that still wouldn't change that the FSF and Copyleft are explicitly anti-proprietary, not intending to be 'no restrictions'.
Your recollection is off, majorly. I'd recommend looking up the origins of the FSF/GPL/Copyleft. The entire movement essentially got started because Stallman gave Symbolics his (public domain) Lisp interpreter, then Symbolics improved it but refused to share the improvements.
"No restrictions" has never been the goal and to claim that they're egoistic hypocrites who are just scared for their own livelihood because of this is just an absurd strawman.
>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.