ckrapu

This is a classic!

Very cool. Excited to try this out sometime!

Isn't this way more expensive?

https://aws.amazon.com/ec2/capacityblocks/pricing/

I think you dramatically underestimate the number of motivated, trained people who find the current societal conditions on Earth untenable.

By the way, this is not just a cheap shot at the Trump administration. The issues are much wider than that.

As you pointed out, the viable area with a sufficiently cold winter is probably shrinking every year. Perhaps the better solution is the one proposed by Yablonovitch et al in which they suggest using deserts instead, though I think that is about as risky, and also a tremendous risk for fire.

Deep down, I have the gut feeling that in a century's time, people will think we were clinically insane for not trying to stockpile as much carbon as possible in whatever means we have, since it is clearly the foundation for biology, and widespread synthetic biology will use it (and N, and P) by the gigatonne. I really don't see any fundamental physical reason why we won't be growing entire cities made out of wood with the right genetic engineering and sufficient feedstock.

Thanks for taking a look at that article! In short, you're right on the first item; only a fraction of that total arable land actually experiences subfreezing winter temperatures.

Given your word choice, I suspect you hail from Britain or a commonwealth country and your (appropriate) definition of township differs from mine. I should have defined it as "survey township" which is a USA term for a grouping of land parcels six miles tall by six miles wide. Again, the number presented is a ballpark estimate, as though we may not have as many nice villages and hedgerows in the Dakotas and other Plains states, we similarly do not farm every single literal acre--though many act as if we should, animals and people be damned.

It'd be nice if you just wrote the article yourself instead of using ChatGPT. I know it's a lot of work, but I would be much more inclined to finish it.

I think that there's a major difference in the resulting mindsets that the two types of experiences form, though.

The first learn that nature is always present and doing its best to kill you / wreck your harvest, and that it is only through man's intelligence and social bonds that we thrive. I would argue a corollary of this is that one cannot tolerate malicious or grossly neglectful people around.

The second group learns that other people are a liability and that bad actors are just a fact of life to be tolerated and worked around.

Both approaches are clearly optimal for their respective environment. The former seems like a stronger foundation for building a civilization on, though.

I can tell you wrote the article with ChatGPT. I’m out as soon as I pick up the smell. I don’t dislike the usage of AI, I just don’t trust. It if you haven’t written it yourself.

I expect it will be popular to dogpile on this article and point out how it's wrong in all sort of ways. I don't mean to do this and appreciate the writing, but a core difference is that software engineering always strives to avoid catering to the idiosyncrasies of the time and place while civil engineering is virtually all about the quirks of the site.

I think this results in quite different cultures.

My point is not that oil fails to generate revenue. Clearly it is a lucrative business. Instead, my claim is that the state economy was remarkably robust, productive, healthy, and well-optimized for middle class quality of life pre-2007.

Does it sound surprising to you that it was perfectly normal to rent a perfect acceptable two bedroom apartment in a safe town on the interstate for $300 a month and still easily find dignified, decent paying jobs without 1000 applications?

I've lived in many cities and work in tech now, and I can confidently say that, as it concerns the professions and jobs that unambiguously sustain and improve life, no community on the planet was more productive than my home state. There is more to the story than some shale.

With all due respect, I disagree.

I loved the winters. I loved the people. I loved how its natural beauty was subtle and rewarded the patient, unlike El Capitan or the Black Hills. The economy was fine before oil appeared.

I don't like this article. In particular, this section is especially poor:

Block 1: We couldn't calculate fast enough. Solution: The GPU.

Block 2: We couldn't train deep enough. Solution: Transformer architecture.

Block 3: We can't "think" fast enough. Solution: Groq’s LPU.

#2 is outright wrong. Deep networks were made viable from residual layers and their refinement. #3 is also incorrect; "think" = compute so this is the same statement as #1.

Also, the "limestone" analogy is pretty weak.

I originally tried to do this on my own server but my GPU is too old :(

I wanted to let an LLM be able to grep and read through it.

I wasn't aware of dots when I wrote the blog post. This is really good to know!! I would like to try again with some newer models.

A thought I often have - older millennials and younger Gen X have a unique obligation to fix certain parts of society because we are the youngest generation old enough to remember how to operate in and enjoy a world that wasn't A/B tested into a gray, lifeless background hum.

From this perspective, humans are just ECC with an optimally non-zero bit flip rate for evolution.

I think Theano takes the crown as first modern end-user library for autodiff and tensor operations.

Mobile experience is not great

I agree with some of the author’s criticisms, but diversity citations are a minor concern compared to the idea of paid access to publicly funded research.

I want to help teach basic application design for GenAI. I've got a basic repo setup, but it's largely based on my own developer experience.

Any feedback is super appreciated.

I'm aiming it at new college grads who haven't had to maintain several services at once in production.

Do you think the basic physics and sensor tolerances would let you go to 10^-2 meters if the environment (e.g. wifi station placements, location of RF-interfering elements) was designed by you?

The title is true if there is no meaningful selection or attrition process for your degree. Otherwise, I disagree strongly.

I think that one’s opinion of this article’s premise is really an index into what your program was like.

Nice idea. The AI infra trend was pretty bad, though. Where are coreweave, nebius, supermicro and others like them? The selection of companies seems like it came from an LLM without any knowledge of the last two years.

I love the idea but when I tried to reduce "SCARFS" to "CAR" (the right answer) it rejected my choice of S, but let me select the other letters. Looks like a bug. Clean it up and I would love to play more!

This happened to me when I decommissioned an App Service instance on Azure.

I totally forgot that it has a readable (I.e. guessable domain name) because AWS’ equivalent service doesn’t. I also had a company subdomain pointing to it so someone got to put up a malicious page on our domain for a day :(

I do know my writing style has problems. My writing teacher said my style was pretentious, and I try to tone it down.

I sort of take this as a compliment, because I've been writing like this my whole life and if it reads like LLM slop, then there's the implication that the result of all of OpenAI's A/B testing and post-training leads to something like my style which at least means it gets people to engage with it!

My colleagues definitely thought it was a joke at the time.

We had this project (all public research) to classify buildings and identify their different subsystems (e.g. load-bearing structure, roof type, ventilation type) to figure out the expected casualties if there was a WMD event of some type. We could get decent data for much of the world, but for some places we had literally nothing beyond a tiny picture of it from satellite imagery.

I had been playing with using GPT-3 to try to have it autocomplete forms like the following. This was 2021 before we had good APIs for instruct models, so this was just straight up letting the LLM regurgitate after pretraining. Here was the type of prompt we used:

""" Engineering building report for building located at 123, X Street, Knoxville TN Prepared by Benjamin Lee, FE --- Building footprint area: 1200 m2 Roof type: built-up roofing Facade material: brick HVAC present: """

Surprisingly (at the time), this was a decent prior. You could also add all sorts of one-off points of interest and amenities like swimming pools and other trivia to help guide the conditional probabilities.

I did make a comment to that effect at the end