Hehe, good point.

people need to read more code, play around with it, break it and fix it to become better programmers.

I think AI bots can help with that. It’s easier now to play around with code which you could not write by yourself, and quickly explore different approaches. And while you might shy away from asking your colleagues a noob question, ChatGPT will happily elaborate.

In the end, it’s just one more tool in the box. We need to learn when and how to use it wisely.

While possible, it goes against the Mediocrity principle:

The idea is to assume mediocrity, rather than starting with the assumption that a phenomenon is special, privileged, exceptional, or even superior.[2][3]

Much like that comment. Can you give a better example, or express why it’s a bad example? That would bring some quality in.

FYI you can self-host GitLab, for example in a Docker container.

You can find “piggy power” at the bottom of the article, headlined “How to describe your game instead”.

Pixel Washer is a cozy, zen-like game where you play as a cute *piggy power washing* beautiful pixelated worlds.

I can read it in two ways: Either you’re a ghostly piggy power, who is washing. Or you’re a “piggy”, who is “power washing”. The grammar is ambiguous.

Maybe you meant to take side for the interpretation as a “cute piggy”. I agree that’s the most likely interpretation.

Still, this might confuse or downright misinform some readers. The main point of the article was to communicate what the game is in a clearer, more accessible way. So I found it worthwhile pointing out how it kind of fails there.

The author was concerned somebody might read a description like “Pixel Washer is like PowerWash Simulator meets Stardew Valley”, and partially fail to understand it, because they don’t really know what “PowerWash Simulator” or “Stardew Valley” are. Because they aren’t literate enough in game titles.

But similarly, one can worry readers might not know certain words or grammatical constructions (maybe because they are no native speakers, or for other reasons), to decide wether it’s a washing power or a piggy washing; because they aren’t literate enough in English.

Describing your game by listing other games is tempting, but not a good idea, and I’m about to convince you why.

That did not age so well. I found most arguments rather weak. Here’s an overview of all the three arguments, copied from the article:

1. It requires your audience to be familiar with those games
2. It creates pre-conceived notions, setting high expectations
3. Players prefer to discover the similarities on their own

Generally, we have at least two options for describing thing A: We can relate it to another thing B (“Pixel washer is like Stardew Valley”), or we can relate it to some abstract attribute (“Pixel washer is uplifting”). Either way, we use language shorthands to describe similarities with other known entities.

About 1: Yes, that is obviously true. And it’s also true for the opposite, when you don’t relate your game to other games. Granted, your description becomes more accessible to a broader audience since it does not require them to know the other games. But instead, the reader now has to be able to understand and visualize what your description might look and feel like as a game (and thus becomes less accessible again). Take for example the first sentence of the proposed better description:

“Pixel Washer is a cozy, zen-like game where you play as a cute piggy power washing beautiful pixelated worlds.”

I’d flag ‘cozy’ and ‘zen-like’ as probably rather less known and/or well-understood terms. I’m also not sure what ‘piggy power’ means. Is it even meant as one thing or is english grammar misleading as so often? Does it involve actual pigs or only their powers, whatever that might mean? But fair enough, even if all that remains not understood, the minimal takeaway is probably that it’s a game with pixels and pigs and washing. So yeah, the alternate description probably works for most people.

But in the same way, a description referring to other games also works for most people.

In case of unclear references, a game-reference wins over a word-description. Like when I look up ‘cozy’ and ‘zen-like’, I may or may not come across definitions and pictures which convey the same idea as the author intended. For example, I might find results about baking cookies or shooting arrows, which have nothing to do with washing pigs. Whereas, when I look up “PowerWash Simulator” and “Stardew Valley”, the results are far less ambiguous.

Argument 2 is the strongest from my point of view. But again, it’s pretty similar for both ways. It should be kept in mind. Maybe it’s best to ask your game testers how they would describe the game, including those who don’t like it, to avoid setting too high expectations because you fell in love with your game while making it.

Argument 3 was entirely new to me. It never crossed my mind, nor did I hear anyone complain about it. I think people very much appreciate language shorthands, if they are used well and are not misleading. If so, they can save time and give a crisp description. And let’s not forget that we are talking about advertisement. We know we are being lied to, that a ‘fast-paced action shooter’ can feel dull and boring quickly. As the author points out, these descriptions serve one purpose only; to generate more sales.

I also wanted to include a reference to Roguelikes or Roguelites. Apparently there once was a game named ‘Rogue’, which no one knows. But it spurred other creators to make something similar, and now we have genres called Roguelike and Roguelite. I think that’s kind of funny in this context, since in this case you somewhat cannot describe the genre without comparing it to another, specific game.

Last but not least, the whole argument is probably less relevant in mainstream games, but more so in indie, or niche, new games in a creative way. When there is almost nothing which is very similar, comparisons to other games might work less well than if you’re just releasing another RTS or FPS.

While you guys kind of have a point, the specific argument you put forward is rather weak. Transportation accounts for an almost negligible part of the overall emissions of a product. Bulk freight cargo is super efficient. If you want to moan about transportation emissions, look at single people sitting in tons of steel making short trips.

The point you still have is that emissions are caused in the process of satisfying a demand. Consumers do have a partial responsibility. However I would object in that the problem cannot be solved from the consumer’s position. It is a market failure. Markets have no incentive to internalize their externalities, that has to come from a different place; e.g. politics. Carbon pricing is an interesting mechanic, since it utilizes that same argument for good.

That’s true. A lot more could be said about this, on various levels in various directions. Ultimately I don’t think this systemic crisis can be solved on a consumer level. The attempt leads to the status quo; different subcultures with some people paying extra to calm their consciousness, while most don’t care or cannot afford. I’m afraid if we try to work with individual sacrifice against economic incentives, the latter will win.

It’s also true that some companies use their economic power as a political lever, to influence legislation in their favor. Or as a societal lever, to sway public opinion in their favor. I guess this meme here tries to address that. I honor the motive. Just the chosen vehicle is broken. With mountains of evidence supporting the cause, however, there are plenty of other, perfectly fine vehicles available.

This meme is so wrong it is deliberate misinformation. The Guardian made an article which is probably this meme’s source. It even linked to the original source, the Carbon Majors Report. But blatantly misquoted the CMR. For example, CMR says something like “100 fossil fuel producers responsible for 71% of industrial GHG emissions”, but The Guardian (and meme posters) omit the italic bits.

What do they mean with producers? Not companies like Apple or Heinz, but simply organizations which produce fossil fuels. Duh. Shell, BP, but also entities like China’s coal sector (which they count as one producer, although it consists of many entities). CMR also states 3rd type emissions are included. Which means emissions caused by “using” their “products”, e.g. you burning gasoline in your car.

So yes, the downvoted guy saying “Consumer emissions and corporate emissions are the same emissions” is pretty spot on in this case, albeit most likely by accident. Rejected not for being wrong, but for not fitting into a narrative, which I call the wrong reasons. Please check your sources before posting. We live in a post-factual world where only narratives count and truth is just another feeling, because of “journalism” and reposts like this. Which is the infuriating part in this particular case. I guess you want to spread awareness about the climate crisis, which is good, but you cannot do so by propagandizing science and spreading lies.

All that from the top of my head. Both the ominous TG article and the fairly short report are easy to find. In just a couple of minutes you can check and confirm how criminally misquoted it was.

Eventually, things settle at almost perfect ratios. Everything between creates some kind of friction.

What does it even mean to bruteforce creating art? Trying all the possible prompts to some image model?

Doesn’t have to be that random, but can be. Here, I wrote: “throw loads of computation power, gazillions of try & error, petabytes of data including human opinions”.

The approach people take to learning or applying a skill like painting is not bruteforcing, there is actual structure and method to it.

Ok, but isn’t that rather an argument that it can eventually be mastered by a machine? They excel at applying structure and method, with far more accuracy (or the precise amount of desired randomness) and speed than we can.

The idea of brute forcing art comes down to philosophical questions. Do we have some immaterial genie in us, which cannot be seen and described by science, which cannot be recreated by engineers? Engeniers, lol. Is art something which depends on who created it, or does it depend on who views it?

Either way what I meant is that it is thinkable that more computation power and better algorithms bring machines closer to being art creators, although some humans surely will reject that solely based on them being machines. Time will tell.

That depends on things we don’t know yet. If it can be brute forced (throw loads of computation power, gazillions of try & error, petabytes of data including human opinions), then yes, “lots of work” can be an equivalent.

If it does not, we have a mystery to solve. Where does this magic come from? It cannot be broken down into data and algorithms, but still emerges in the material world? How? And what is it, if not dependent on knowledge stored in matter?

On the other hand, how do humans come up with good, meaningful art? Talent Practice. Isn’t that just another equivalent of “lots of work”? This magic depends on many learned data points and acquired algorithms, executed by human brains.

There also is survivor bias. Millions of people practice art, but only a tiny fraction is recognized as artists (if you ask the magazines and wallets). Would we apply the same measure to computer generated art, or would we expect them to shine in every instance?

As “good, meaningful art” still lacks a good, meaningful definition, I can see humans moving the goalpost as technology progresses, so that it always remains a human domain. We just like to feel special and have a hard time accepting humiliations like being pushed out of the center of the solar system, or placed on one random planet among billion others, or being just one of many animal species.

Or maybe we are unique in this case. We’ll probably be wiser in a few decades.

This ambiguity is what I had in mind when I read “let me be clear”. Though now I get it.

You can use more debug outputs (log(…)) to narrow it down. Challenge your assumptions! If necessary, check line by line if all the variables still behave as expected. Or use a debugger if available/familiar.

This takes a few minutes tops and guarantees you to find at which line the actual behaviour diverts from your expectations. Then, you can make a more precise search. But usually the solution is obvious once you have found the precise cause.

Sustainability loses it’s meaning in space, or needs a whole different meaning. There are no ecosystems on other planets or moons which we could tip over with unsustainable practices.

Just throwing this out without addressing the glaring differences looks like uninformed romanticism.

Claims it is a literature movement. Fails to give a single example of a book that can be defined as solarpunk or that self-defines as such.

Really!? Wow, that is poor. Thanks for saving my time!

Edit: And for the update.

Romantically speaking, aren’t we all just genitals with attached life support?

I didn’t know about that [under this name], so thanks for bringing it up. But no, I meant something slightly different.

Colors of noise describes how to generate different distributions. What I meant was how to transform distributions.

Many of the examples in the article start with a random number distribution, and then transform it to reduce discrepancy.

This reminded me of audio/video signal processing. For example, one can take a picture and transform it to reduce discrepancy (so that neither very bright parts nor very dark parts overshoot). Or you can take an audio sample and transform it to reduce discrepancy in loudness.

So the idea was that maybe techniques of either field (RNG, audio, video) could be applied to both other fields.

That’s a very long list of different techniques with examples, very cool!

Though I wonder, is there some connection to image processing, high dynamic range?

Or audio compression, the kind which brings out the kick in the mix, not the kind which saves disk space?

The similarity I see between all three fields is, they try to bring down extreme values, outliers, to level the field, while still retaining characteristics.

Thank you, great post!

I recognized the constellation. Though it’s good to have a refresher, and I learned something new.