An entirely different form of research could be done by sending large quantities of normal people into space. Astronauts are such a small sample size (and so thoroughly vetted) that you get a different statistical view.
The price at the pump affects not only a voter's commuter car, but also every truck that delivers goods across the US. This may have a much larger knock-on effect.
OTOH the US is the largest oil producer in the world [1]. Theoretically the US could keep domestic prices in check, but that would require rather drastic administrative pressure, likely only legal at wartime.
Need a d10 roll? Just look at the last digit of the current second on your clock. Is it random? No, but it approximates randomness if you only make a roll sporadically.
Use hundredths of a second on the stopwatch. With a little math, and throwing out invalid results, you can generate a random number in any range < 100.
Though I do wonder if the hundredths are true or just for show. Maybe they're randomly chosen. :)
It’s pseudorandom. It’s predictable in theory because if you had another watch, or an amazing sense of time, you could predict it. Is that realistic? Not really.
Computers use their clock to generate pseudorandom numbers all the time (hehe). It’s great randomness for something like shuffling songs or a sorting algorithm. You don’t want to use it with some “adversarial”, like online poker.
For sure. The more specialized or obscure of things you have to do, the less LLMs help you.
Building a simple marketing website? Probably don’t waste your time - an LLM will probably be faster.
Designing a new SLAM algorithm? Probably LLMs will spin around in circles helplessly. That being said, that was my experience several years ago… maybe state of the art has changed in the computer vision space.
> The more specialized or obscure of things you have to do, the less LLMs help you.
I've been impressed by how this isn't quite true. A lot of my coding life is spent in the popular languages, which the LLMs obviously excel at.
But a random dates-to-the-80s robotics language (Karel)? I unfortunately have to use it sometimes, and Claude ingested a 100s of pages long PDF manual for the language and now it's better at it than I am. It doesn't even have a compiler to test against, and still it rarely makes mistakes.
I think the trick with a lot of these LLMs is just figuring out the best techniques for using them. Fortunately a lot of people are working all the time to figure this out.
Agreed. This sentiment you are replying to is a common one and is just people self-aggrandizing. No, almost nobody is working on code novel enough to be difficult for an LLM. All code projects build on things LLM's understand very well.
Even if your architectural idea is completely unique... a never before seen magnum opus, the building blocks are still legos.
Specialized is probably not the word I'd use, because llms are generally useful to understand more specialized / obscure topics. For example I've never randomly heard people talking about the dicom standard, llms have no trouble with it.
I think there is a sweet spot for the training(?) on these LLMs where there is basically only "professional" level documentation and chatter, without the layman stuff being picked up from reddit and github/etc.
I was looking at trying to remember/figure out some obscure hardware communication protocol to figure out enumeration of a hardware bus on some servers. Feeding codex a few RFC URLs and other such information, plus telling it to search the internet resulted in extremely rapid progress vs. having to wade through 500 pages of technical jargon and specification documents.
I'm sure if I was extending the spec to a 3.0 version in hardware or something it would not be useful, but for someone who just needs to understand the basics to get some quick tooling stood up it was close to magic.
The standard for obscurity is different for LLMs, something can be very widespread and public without the average person knowing about it. DICOM is used at practically every hospital in the world, there's whole websites dedicated to browsing the documentation, companies employ people solely for DICOM work, there's popular maintained libraries for several different languages, etc, so the LLM has an enormous amount of it in its training data.
The question relevant for LLMs would be "how many high quality results would I get if I googled something related to this", and for DICOM the answer is "many". As long the that is the case LLMs will not have trouble answering questions about it either.
One tendency I've noticed is that LLMs struggle with creativity. If you give them a language with extremely powerful and expressive features, they'll often fail to use them to simplify other problems the way a good programmer does. Wolfram is a language essentially designed around that.
I wasn't able to replicate in my own testing though. Do you know if it also fails for "mathematica" code? There's much more text online about that.
> Building a simple marketing website? Probably don’t waste your time - an LLM will probably be faster.
This is actually where I would be most reluctant to use an LLM. Your website represents your product, and you probably don’t want to give it the scent of homogenized AI slop. People can tell.
They can tell if you let it use whatever CSS it wants (Claude will nearly always make a purple or blue website with gross rainbow gradients). They can also tell if you let it write your marketing copy.
If you decide on your own brand colors and wording, there’s very little left about the code that can’t be done instantly by an LLM (at least on a marketing website).
I was asking earlier if DHI has tests - seems they do. As does the FastAPI refactor.
Would be nice though if there was an exact test suite that ran against both that could demonstrate parity. It would be onerous to try and compare both library’s test to see which one is more comprehensive (believable) - by default I’d expect the original to be the most believable (duh) mostly because tests represent historical cases that can up and broke it.
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