Grading on a curve was meant to hide the fact that some teachers couldn't teach, some could, some wouldn't, and others would. It protected the professor at the expense of the students' education.

And it ruins grades as a marker of achievement or ability. From a student's perspective, if I pay for a course, the result should be that my grade reflects the degree to which I've mastered the material, not the variations between the quality of the students and the quality of the instruction. Grading on a curve allows a deadbeat professor and a deadbeat class to essentially turn the class into a credential mill without the necessity of education.

Students can safely assume that courses graded on a curve are staffed by incompetent or lazy professors, taken by lazy or incompetent students, or quite possibly both. When I was in university, this type of grading was used most often in the general education electives, where the professors didn't really care about the students, and the students didn't care about the subject. To adopt the same approach for mainline courses is to transform the entire university from a place of learning into a credentials broker or diploma mill.

Really? Doesn't feel that way at all to me. What it feels like is that LLMs are a massive force multiplier for the skills I already have.

Why the scare quotes? Are you uncertain if she's a woman? Or do you not understand how to use quotation marks?

Did they steal stuff worth millions?

Though, of course, this raises the question of why someone would leave valuable masters in a suitcase in an unoccupied core.

Actually, at the extreme scales, which is the total volume of the observable universe, the universe is quite homogeneous. As I recall, to the order of 1-in-10000 variance. This is why Inflationary cosmology was developed, to explain the distinct lack of lumpiness in the universe, which is what we would expect if the Big Bang alone were responsible.

Dude, I've been writing code for 40 years. I've used so many different tools, stacks, libraries and APIs that at this point I don't remember any of them, and I haven't remembered them for years, and it doesn't matter at all. Sure, I have to look everything up, but that's fine, that doesn't matter. What matters is that I know when something looks wrong, or hard to maintain, or inefficient, or insecure, or... pick the axis. And I can dig in and find the problem. Anyone can tell if code works, that's easy. Understanding when and why it might break or otherwise impose additional costs, that's the real skill.

Which, as it happens, is exactly the skill you need to use an LLM effectively. Also the skill you need to understand legacy code, review colleagues' commits, etc., etc., etc. I used to say that the ability to read and understand code is an underrated skill, but an old friend corrected me at lunch a couple of weeks ago, saying that the ability to read and understand code is the most important software engineering skill, and always has been. Upon reflection, I agreed. And LLMs make this clearer than ever before.

Weekend fun car would be the summer car.
Remember, the winter car is used for dirty tasks even in the summer.

I'm confused by all of these posters who've never heard of "the curve". I wonder if it's because they're all young'uns who went to school during an interval when progressive educators had decided to abandon it... with the inevitable grade inflation that was totally predicted.

When I was in college 40 years ago every class that had more than ~30 students in it was graded on the curve, and even some of the smaller ones (though in a smaller class it becomes statistically questionable). I used to love the groans when a buddy and I walked in the first day of class, because they knew we were going to "bend the curve". :D

Though in all seriousness, the whole point of using a curve is that in a sufficiently large class, it's statistically guaranteed (to very high probability) to be "unbendable". Inserting a couple of students at the top would mean that students who might be just above a cutoff might fall just below it, but it's not going to make a difference to many.

College isn't like tee-ball, where every player gets a participation trophy, and it shouldn't be. Especially not at elite schools. Part of the purpose of a university education is to act as a filter, and not just to filter out those who can't cut it at all, but to rank all of the students by performance, so graduate schools and potential employers can make use of that information.

I suspect part of it is that the mitigation for DirtyFrag covers it, so everyone who blocked all the modules in question when that had only an incomplete patch probably hasn't unblocked them yet. I think this is the 4th patch for these modules, and only got a new name rather than just "there's still a way to get this code to do the wrong thing" because a different outside team found this one.

Don't be so sure about the "shipped off". Google is heavily investing in on-device AI that runs in a trusted enclave (e.g. TrustZone on ARM). I left the company in August of last year but I doubt this has changed since it's been a major area of focus for quite some time.

With respect to being a "lying snake," this is the C-Suite, not rank and file employees. When it comes to the C-Suite, such behavior is generally accepted, if not expected, and hardly a reason for firing.

I mean, can you even name the last time a CEO was fired for lying?

The biggest problem with replicating CUDA is not the technical aspects, but finding VC with enough brains to know whom to hire. Most CS grads have the knowledge, but not the drive. Most liberal arts grads have the drive, the creativity, but not the knowledge. You need to find one with both, because creating the next Nvidia killer will require someone who is boring enough to reinvent the wheel, but has enough creativity to find novel solutions to performance problems.

The computer science and hardware engineering behind the hardware and software (Nvidia/CUDA) have been known for decades. The Nvidia hardware could be replicated with FPGAs - notwithstanding any patents Nvidia might have. The software API could be replicated rather easily; parallelism has been known and studied in computer engineering (again) for decades now. What Nvidia did was political - they provided both the hardware and the API to easily use it in one package which could be understood by the C-Suite class. The challenge was never technical, but marketing.

More specifically, you'd need to understand how compilers work, and how to use YACC or bison, or something similar to generate the compiler code for you. You'd have to understand digital logic and how to create logic functions with NAND gates. If you see an FPGA development kit, know what it is, and think to yourself, "What I could do with that..." you're probably a good fit for the job. And you'd need someone willing to bankroll your project until you could demonstrate that you beat Nvidia on something marketable - like floating point performance. Or power consumption.

From an engineering standpoint, what Nvidia has done is trivial - because the solution could be reproduced by an engineer using already known techniques. But what Nvidia did was to combine technical knowledge with an understanding of their market to produce the dominant position they have today. Any computer engineer worth his diploma could produce a design with FPGAs that would beat Nvidia GPUs, but Nvidia did it first.

So even hackers can be replaced by AI now? What is this world coming to?!

I don't need 1000 miles. 600 (unencumbered) is definitely sufficient, and 500 might be okay. The thing is that I'll lose half to 2/3 of that range when towing my camp trailer, and that's not even considering that I'm typically towing it up into the mountains, gaining ~5000 vertical feet. I also need minimum 12k pounds of towing capacity and I'd like a little headroom, so call it 16k, and the bed payload has to be able to take at least 2000 pounds, because that's how much the trailer puts on the fifth-wheel hitch.

I'm anxiously awaiting an EV pickup that can do this. I'd love to have essentially unllimited electricity to buffer cloudy days (I have 1 kW of solar panels on the trailer and on sunny days they generate way more than enough, but consecutive cloudy days can leave be difficult).

3/4 ton and 1-ton gas and diesel pickups typically have oversized fuel tanks that provide about 600 miles of range, because that's what you actually need when you start hauling or towing significant loads. I don't think an EV pickup needs to have more range, but it needs to be comparable, and to be able to tow and haul comparable loads.

I'm not anti-EV by any means. I bought my first EV in 2011, and have had electric cars ever since. Trucks are a different sort of problem, though.

That's getting there, though I'd like to see some driving tests with a good-sized fifth wheel at highway speeds. The towing capacity is probably okay, though it provides very little headroom for when I'm towing both my camp trailer (~8k) and my boat (~3.5k), which I actually do several times each summer. But I think the payload capacity is too small to tow the trailer, which puts about 2000 points on the truck.