abetusk

I'm having trouble finding it but there was an article a while back comparing mp3 speed gains from hardware and software. The hardware was getting better at the pace of Moore's law but the (de)compression speeds for mp3s were far outpacing it, showing that mp3 decoding optimizations were giving orders of magnitude more speedup.

I think this is a common trend for algorithms that are in wide use, where you have Moore's law like behavior on the hardware side and then also software optimizations that give their own order of magnitude speedups.

The motivation is better than most, trying to treat infinity as just another case instead of an exceptional one, but, to me at least, it still feels like a "hack" to put in a third dimension and then insist on dividing by it to reproject whenever you want to do anything useful.

I'm a little used to the trick but it'd be nice to give some deeper motivation of why the trick works in the first place and why this particular representation.

Note that I recently tried to sign up so that I could answer/update some questions that I have expertise in and was refused because my email address wasn't recognized.

ETH is false! I really would not have believed it.

[0]:

> We give a deterministic algorithm for exactly counting the satisfying assignments of an arbitrary k-CNF formula. For every fixed k ⩾ 2, on a formula with n variables and m clauses, our algorithm uses $poly_k (n, m)$ space and runs in time $(n + m)^{O_k(1)} 2^{n/(\log n)^{1/2 + o(1)}}$

The dominant term there is (roughly) $2^{n / (\log n)^{0.5}}$, so still superpolynomial but less than exponential. Fucking wild.

[0] https://github.com/openai/math/blob/main/preprints/The-Expon...

> But for many “pure” mathematicians ... machines can discover the truths we’re seeking, at the cost of robbing us of the struggle and joy of discovering them ourselves.

This sentiment is akin to wanting to curse humanity to darkness because they're not the one carrying the candle. Strogatz is not lamenting the loss of discovery, he's lamenting the loss of being heralded as a hero for the discovery.

Nothing prevents him or others discovering truths for themselves. Turn off the computer and try to re-derive it from first principles. Have at it! This is what students do in many courses anyway.

I've found little so depressing as being at the precipice of a renaissance of science and mathematical discovery, only to see how resistant the scientists and mathematicians are to it. It lays bare their counterarguments. Any pretense of wanting to discovery fundamental truth about reality is supplanted with "but what about our careers!?". Strogatz, and others like him, should be ashamed of themselves.

You're right, my bad. It not only talks about IP as being an enclosure idea, it also does talk about FOSS (GPL).

I still disagree but it looks like the author makes a cursory attempt to address the issues that I think are more relevant.

It's hard to imagine what kind of contortions someone would have to go through to come to this conclusion.

Not one word of copyright length, which is now effectively over a century long. Not one word of some of the most successful AI models being open source.

Commons encirclement, as the author is describing, has been happening since source code was labelled art instead of engineering and since lobbying efforts have been extending copyright terms. My view is that AI technology are trampling over the encirclement, which is why so many people are upset.

ah! you have to click on the glowing white square in the middle!

I was rotating all around and couldn't figure out what to actually do.

This definitely needs a tutorial.

Also called slopcore

Very pretty but needs some instructions or other affordances. I have no idea what to do.

Hacker News (news.ycombinator.com) started as a link aggregator/message board for the startup incubator Y Combinator [0]. Essentially a place to share projects, share tech news and other internet sites that appeal to the tech startup community of San Fransisco, though the appeal is much broader (I'm in upstate NY).

I guess it's a bit hard to explain, but there's a cluster of points of interest that appeal to the stereotypical user on hacker news, of which generative art (and potentially a "wave function collapse"-like algorithm that you may or may not be using), architecture, brutalism and indie gamedev are part of that list.

I suspected your project resonates with a lot of people's interest who frequent the site, which is why I submitted it here (anyone can submit, I just happened to be the one that did it first).

[0] https://www.ycombinator.com/

From the comments section [0]:

"""

specs:

Windows 10 / macOS 12+ / Ubuntu 22.04+ Any GPU from the last ~8 years, integrated is fine, M1 Macs are plenty 4 GB RAM ~250 MB disk

It's a small game, no dedicated graphics card needed.

"""

[0] https://placeholders.itch.io/tiny-brutalism#post-17475584

I find it interesting that the average energy consumption of a person (or household?) is roughly 30kWh/day and the energy usage of a car is roughly 40kWh/day. I wonder if it's some kind of truism that our transportation needs roughly equal our energy needs.

I'd be interested to know if that's just a coincidence or whether there's an underlying reason.

code: https://github.com/eshin087/gcdatlas

lg(n!) grows roughly as (n lg n). Constants matter, of course, but to that's the rough estimate.

As an aside, if you take numbers from 0 to (n-1) in an array, there are n! configurations, so representing each configuration or differentiating each configuration take n lg n bits. So, in some sense, taking a mapping that's able to differentiate the input state to map to the ordered state takes at least O(n lg n) time, the standard runtime of a basic sorting algorithm.

Any additional assumptions (n larger than maximum element, distribution of elements) helps reduce this.

What is the other clay prize that's might be solved now/soon?

Do you have a repo for this project? I would encourage you to work in the open, if you're not already.

Even a BOM would be helpful. Also understanding the post-processing/cleanup step would be interesting.

Video lecture discussing sparse automatic differentiation.

Blog post: https://iclr-blogposts.github.io/2025/blog/sparse-autodiff/

Previous discussion: https://news.ycombinator.com/item?id=43828423

paper: https://www.nature.com/articles/s41586-026-10917-6

code: https://github.com/dynamicslab/hydrogym