suresk

Learning is about so much more than accessing information, though. It is about building mental models, resolving ambiguity, exploring things that the source doesn't explain very well, and so much more.

Questions like "What do these lines of code do?" or "How does this fit into the big picture?" or "Wait, this doesn't make sense?" are rarely answered by the source.

This is a bit fresher in my mind in the math domain, but I don't think it is any different in any number of other domains, including coding. I've been working through a math textbook, gotten confused about how the author gets from step 2 to step 3, taken a picture of the text, and had AI explain it to me - it almost always gives me a much better understanding of what is going on and helps make things so much clearer. There is a level of interactivity that can't exist in a book or other "source" of information.

I think there is a bit of tension when it comes to learning and sometimes the struggle itself is informative, but there is a reason people hire tutors and go to classes taught by teachers vs just reading a textbook, and cutting yourself off from a tool because you've seen it be wrong about something seems like a silly mistake.

> Please don't.

I will.

Can these be wrong? Certainly. So can humans. Many of your examples are of humans being wrong. That doesn't make LLMs - or humans - useless. The fact that they are not infallible is not a reason to avoid using them and I'm not going to throw out a tool that has been incredibly valuable to me because someone on the internet got some bad CORS advice.

I've seen a lot of LLM uses that are really just zero/few-shot classifiers with a lot of extra steps, so it is interesting to see more models that are taking advantage of all the intelligence encoded in the latent spaces of these models with really efficient output. It feels like this is an under-explored area of LLMs right now and I'm excited to see what comes out of it.

I've found sorta the opposite - in any area, it can just do everything for you, or it can be an incredible teacher. I've been re-learning a lot of higher-level math and it has been an knowledgeable, infinitely patient, always-available tutor. Of course, I could just have it do just about any math I want for me, but that's not the point.

Kinda the same with language/technology stuff - it can be a great tutor and it can scaffold other parts of a project for you. It can give you feedback and let you focus on the interesting parts.

I guess the motivation itself may be hard because of the fear of it taking over much of our jobs, but having this kind of help/feedback is pretty cool for the sake of learning things just because they are interesting!

The opposite problem can happen- the CEO uses the product all the time and becomes blind to problems. “It has always worked that way”, or “who would want to do that!?”” are much more common than pure apathy.

They also get massive subsidies and tax breaks for building these data centers. They require the negotiations be done in secret and often fight to keep the agreements secret to make it so people don’t flip out when they see how bad they are.

> I'm surprised you're not touting the "save on your power bill" benefits.

At ~$600/kWh for capacity, the ROI isn't great. I have a pretty big differential on my rates because I have an EV, and even then I'd need over a decade to make the $1,000 back assuming I fully discharged it every day.

I've only done the CUDA side (and not professionally), so I've always wondered how much those skills transfer either way myself. I imagine some of the specific techniques employed are fairly different, but a lot of it is just your mental model for programming, which can be a bit of a shift if you're not used to it.

I'd think things like optimizing for occupancy/memory throughput, ensuring coalesced memory accesses, tuning block sizes, using fast math alternatives, writing parallel algorithms, working with profiling tools like nsight, and things like that are fairly transferable?

So many fond memories of this game - it was a really fun blend of railroad sim and economic sim that I haven't really found since. I'll never forget the "ding ding ding" sound that goes off when a train pulls into a station and earns you a bit of cash!

I was having trouble finding an E Core die shot, but that helps put it into perspective a bit anyway. Thanks!

My non-expert brain immediately jumped to double-pumping + maybe working with their thread director to have tasks using a lot of AVX512 instructions prefer P cores more. It feels like such an obvious solution to a really dumb problem that I assumed there was something simple I was missing.

The register file size makes sense, I didn't think they were that much of the die on those processors but I guess they had to be pretty aggressive to meet power goals?

Having dabbled in CUDA, but not worked on it professionally, it feels like a lot of the complexity isn't really in CUDA/C++, but in the algorithms you have to come up with to really take advantage of the hardware.

Optimizing something for SIMD execution isn't often straightforward and it isn't something a lot of developers encounter outside a few small areas. There are also a lot of hardware architecture considerations you have to work with (memory transfer speed is a big one) to even come close to saturating the compute units.