There are many articles extolling the virtues of Lisp. I would like to see some articles that have a level-headed criticisms or critique of Lisp, it's ideas and it's place in the ecosystem of languages.
Articles like this, and the PG articles it references, amount to "if you know, you know". I understand the appeal and I understand the explicit and implicit arguments this article is making.
Computer programming has matured quite a bit in the past 60 years. I would like to see more articles that are more considered in their examination.
I certainly don't have deep knowledge in this area but my understanding is that the cartridge is basically a cavity with a small hole at the bottom and a piezo electric element at the top to sputter liquid down. The tolerances are tight but considering how advanced manufacturing is, I would imagine someone competent could design and prototype it with the tools and manufacturing ecosystem that's available now.
There's at least one project that has tried to design an actual DIY/open-source inkjet printer [0], along with inkjet print heads [1].
The ink most likely need some special sauce? But I imagine there's many organizations that can specialize in making the ink so that it can be treated close to a commodity. If not, maybe this can also be engineered?
There's a long history of proving results like this.
NP-Completeness is the norm, not the exception. Any system that's complex enough is almost surely NP-Complete. For similar reasons, Turing Machine Equivalence is also the norm, not the exception.
These results are interesting but not unexpected. A more interesting question is under what conditions is the problem difficult to find solutions for. Many NP-Complete instance ensembles turn out to effectively have polynomial time solutions (3-SAT w/ uniform clause variable choice, Hamilton Cycles in Erdos-Renyi random graphs), so proving NP-Completeness is not a death knell for approximation.
Ron Maimon uses an argument that relies purely on symmetry, which circumvents the standard explanations, including many in this thread. In some sense, this is the simplified version of Noether's theorem (as far as I understand it).
As an aside, I believe Ron Maimon's account was suspended after he challenged the character of someone who was soliciting votes for a moderator position. Ron Maimon's stance was that if someone was running for an elected position, discussing their character was valid. The SO site had/has a strict challenge-the-question-not-the-person policy, which the moderators used to ban him permanently.
At the time, I remember seeing some posts by Ron talking about how the SO sites were corrupted by their policies and that it was a matter of time before they ceased to provide value. I think this was late 2000s or early 2010s. Looking back it's hard not to feel like his stance was prescient.
I was careful to say "Moore's law-like". Moore's law is stated as "transistor count per area doubles about every 2 years." [0] As stated, then yes, this might be true but, while important, that's not really the quantity we care about.
As you point out it's really cost per transistor or cost per flop that we mostly care about. I'm finding it hard to find a succinct and clear plot, but I believe one is provided by Our World In Data on "GPU computational performance per dollar" [1] which, to my eyes, clearly shows exponential growth in computational power per dollar.
The picture for storage is a little more muddied but if you squint just right you might still be able to recover an uninterrupted exponential growth [2].
In my view, it's pretty clear that advances in AI have progressed so quickly because GPUs have been keeping up with the exponential growth of computational power (per unit cost).
Exponential growth in this area is usually characterized by "S-curves", where one technology gets saturated but the exponential increase in power or decrease in cost is picked up by another, adjacent, technology, that allows the growth to continue. For compute it's CPUs to GPUs. For storage it's platter drives that are now being overtaken by SSDs.
The more general phenomena is called Wright's law, or experience curve effects [3].
Moore's law or one of its generalizations still holds, so it will only be a short matter of time before a $1k computer will be able to train and run a powerful enough model.
I appreciate where the author is coming from, as we often have much more context because of training that have since atrophied a bit, but in defense of the dynamic programming solution, this generalizes very well.
As stated, the choose(2n,n) solution of course works but as soon as you deviate from a square, things can get more complicated. What if it's a rectangle? An arbitrary shape? One with holes? The dynamic programming solution takes all of this in stride (assuming, of course, that the conditions of only going right and down still hold).
Pascal's triangle is, after all, a dynamic programming solution. It just so happens that there's a "closed form" solution to their entries.
I'm all for clever tricks but I also appreciate much more a solution that generalizes well and gives more insight into a class of problems.
This is like a modern form of "I could do that in a weekend". Try reading the article before making such statements.
There's a lot of pre-processing, experimentation and validation that went into this project. The training data collection and sanitization alone is a big undertaking.
As for the blog post itself, from the article:
> Note: This blog post is 100% written by me. No AI has been used whatsoever.
Put another way: You can ask the LLM yourself to do this project? Please do, share your prompt, I'd like to see it.
As others mentioned, the link is broken. A quick look at the author's GitHub or HN profile doesn't have anything jump out at me.
I will mention that there was a paper from 2017 on "Bounds on the Satisfiability Threshold for Power Law Distributed Random SAT" that constructed what looks very nearly like a phase diagram for satisfiable instances of SAT.
> OAOL runs commercial proprietary servers and the community was not free to distribute the game or run competing servers during the commercial active period.
Reference? The source was dedicated to the public domain in early 2018, which coincides with the release of the game [0].
> So the model that made this economically viable was the proprietary control model.
This is a complete fabrication.
> Anyway, just because a handful of games can exist on libre models (even given what I've said) that doesn't mean the industry can survive with mandatory libre requirements.
Making a living from open source software is hard, game or no. Making a living as a game developer is hard to begin with and many proprietary games are not commercially successful or viable.
My point was that the ecosystem is a lot more complex than your reductive analysis.
> That... basically kills the entire gaming industry.
Pretty dismissive, no?
Jason Rohrer puts many (most?) of his games in the public domain, including "One Hour, One Life" [0] [1]. As far as I know, his game is pretty successful, by indie standards.
Teeworlds was at one point accepting donations, I believe [2]. Solarus has a donation page [3].
I'm sure there are many more examples that span the spectrum of payment options and cover different permutations of being online or offline.
To me, the deeper question is what are you actually purchasing? The bytes? The convenience? A slice of server resources? Developers and artists time?
I'm happy to give money to projects that I use, especially if it creates less friction than trying to go outside of the payment method and if the project is libre/free. I'm willing to pay for proprietary content but I have little expectation about what kind of service they're providing, especially they fold.
If there's a libre/free option, I would much prefer to invest in it. If there's a proprietary option that is asking for resources, I'm much less prone to give since it's clearly a transactional relationship.
If we're talking about civilizations that have access to energy that's on the order of many stars, the civilization itself can be considered a meta-organism that spans many millennia. Launching probes that take hundreds or thousands of years to report back becomes a small fraction of overall lifespan.