They define "public funding source" later as a list of specific funding organizations like OpenCollective. But that definition feels odd, and like much of this feels like it might be AI generated.
I started knowing about them about 9-10 years ago on youtube. I think it was when they were planning to cross the Pacific Ocean. This was one of the videos I watched https://www.youtube.com/watch?v=pZ9Fqcy7cp0
And then afterwards they resurfaced on my feeds maybe last year, and I got to read the book. I really enjoyed it as well. The episode where you realise the voyage is dangerous and you could lose your life really resonated with me. In my entourage, I think people who have never sailed sometimes under-estimate the dangers and complexity of traveling across a vast ocean ...
Yes that’s the idea, these conjectures basically say “there’s no better algorithm than the naive/brute force one”. It’s like if P!=NP, then there’s no (asymptotically) better algorithm for SAT than naive backtracking search.
3SUM is (was?) one of the key conjectures in fine-grained complexity, mostly used to derive lower bounds for other problems. As such, most did not think a subquadratic algorithm was possible. Similar for APSP
By the same reasoning, why should I care about the output of someone else’s clanker if I can just get it from my own clanker?
But to answer your question, I do still care about software being maintained by someone who can make design decisions instead of just yielding control to bots who tend to produce mediocre designs.
Perhaps we can think of projects like these the same way we’d think of a good tweet - not valuable in itself but valuable because of the ideas or conversations it generates.
Maybe because they know more about the domain than you do, and it will take you a lot more back-and-forth with your clanker than it would for them with their clanker.
And maybe they don't, and that's the problem with one-day-old projects: we know for sure that the programmer didn't have time to learn anything from that particular project and we now have to trust them that they know what they are doing. Unfortunately this is wrong in 90+% of the time, so that's kind of a gamble to expect one particular project to tick this box.
If I understand correctly, one of the recent cool generative AI-assisted results is actually supposed to have closed this problem, complete with a Lean proof!
I think language designers in general have realized that there's nothing particularly virtuous about a programming language with a syntax that's hard to parse.
Go and Rust (and Pascal) have cleaned up C's awful legacy enough that they're parseable with LL(X) parsers that don't have scary corner cases or polynomial behavior.
This can be good advice if worded a different way: only go to grad school if you really love doing research. If you love research, then the right graduate program is the best place for that. Getting a PhD was the hardest thing I’ve ever done but I don’t regret a single day of it.
> Getting a PhD was the hardest thing I’ve ever done but I don’t regret a single day of it.
my phd was tough because my advisors was useless and i do regret it because i lost 4 years of industry high-salary pay. way more common than your experience i think.
The challenge is how do you really know if you love doing research, committing to graduate school, before really doing research? It is hard to know how the sausage is made without spending/doing time at a meat-processing plant. My undergraduate experience doing research, including be published, was much different than the graduate experience. And graduate experience was much different than the assistant professor experience.
It took me two trips to grad school to get a PhD. The first on their terms, I burned out. The second, on my terms, was a much better/different experience.
I had the same experience; grad school is the best preparation for grad school. I had wonderful experiences participating in NSF Research Experiences for Undergraduate projects during two of my summers as an undergrad, and I also had a wonderful eight-month internship in a research lab at a major Japanese IT company, but grad school was a different beast, even though I was a grad student at the place I did my NSF REUs. The internships felt like the “demo version” of the real thing.
Come to think of it, I had similar whiplash in industry of full-time jobs being far more stressful than my internships, which I now realize were more-or-less “demo versions.”
To make a long story short, after grad school and working as a researcher in industry for nearly a decade before becoming a professor at a community college (where the emphasis is teaching), I learned that I love doing research as a hobby but not as a career, where there are many pressures.
I understand your argument for people who pursue research degrees but there are plenty of non-research professional programs that people should be encouraged to pursue if relevant for their goals.
E.g. masters in teaching
SQLite has a list of sponsors right on the homepage https://www.sqlite.org/
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