cogi234's blog

here are a few other "secret" dashboards:

Posts from your "crushes"

"What you missed" (will be empty if you already visit Tumblr every day)

"Trending"

nope! i hovered over the link and this popped up on the bottom of the browser. here's all the links in order:

https://www.tumblr.com/timeline/blog_subscriptions

https://www.tumblr.com/timeline/crushes

https://www.tumblr.com/timeline/what_you_missed

https://www.tumblr.com/timeline/trending

"The law prohibits, currently, on currency, the likeness of any person — not just the president — who is alive," said Jeremy Paul, a professor and former dean at Northeastern University School of Law who specializes in constitutional law.

The Treasury Department argues coins do not apply, citing a law that Congress passed in 2020 authorizing the creation of a special-edition 250th coin.

Paul says the coin question could potentially reach the courts. Either way, he believes, the break with tradition should be cause for concern.

"Regardless of careful parsing of the language of these individual statutes, this plan the president and Secretary Bessent have cooked up is inconsistent with the principles of our country, and it politicizes something that's not supposed to be political," he said.

My definitive ranking of my least favorite bodies of water! These are ranked from least to most scary (1/10 is okay, 10/10 gives me nightmares). I’m sorry this post is long, I have a lot of thoughts and feelings about this.

The Great Blue Hole, Belize

I’ve been here! I have snorkeled over this thing! It is terrifying! The water around the hole is so shallow you can’t even swim over the coral without bumping it, and then there’s a little slope down, and then it just fucking drops off into the abyss! When you’re over the hole the water temperature drops like 10 degrees and it’s midnight blue even when you’re right by the surface. Anyway. The Great Blue Hole is a massive underwater cave, and its roughly 410 feet deep. Overall, it’s a relatively safe area to swim. It’s a popular tourist attraction and recreational divers can even go down and explore some of the caves. People do die at the Blue Hole, but it is generally from a lack of diving experience rather than anything sinister going on down in the depths. My rating for this one is 1/10 because I’ve been here and although it’s kinda freaky it’s really not that bad.

Lake Baikal, Russia

When I want to give myself a scare I look at the depth diagram of this lake. It’s so deep because it’s not a regular lake, it’s a Rift Valley, A massive crack in the earth’s crust where the continental plates are pulling apart. It’s over 5,000 feet deep and contains one-fifth of all freshwater on Earth. Luckily, its not any more deadly than a normal lake. It just happens to be very, very, freakishly deep. My rating for this lake is a 2/10 because I really hate looking at the depth charts but just looking at the lake itself isn’t that scary.

Jacob’s Well, Texas

This “well” is actually the opening to an underwater cave system. It’s roughly 120 feet deep, surrounded by very shallow water. This area is safe to swim in, but diving into the well can be deadly. The cave system below has false exits and narrow passages, resulting in multiple divers getting trapped and dying. My rating is a 3/10, because although I hate seeing that drop into the abyss it’s a pretty safe place to swim as long as you don’t go down into the cave (which I sure as shit won’t).

The Devil’s Kettle, Minnesota

This is an area in the Brule River where half the river just disappears. It literally falls into a hole and is never seen again. Scientists have dropped in dye, ping pong balls, and other things to try and figure out where it goes, and the things they drop in never resurface. Rating is 4/10 because Sometimes I worry I’m going to fall into it.

Flathead Lake, Montana

Everyone has probably seen this picture accompanied by a description about how this lake is actually hundreds of feet deep but just looks shallow because the water is so clear. If that were the case, this would definitely rank higher, but that claim is mostly bull. Look at the shadow of the raft. If it were hundreds of feet deep, the shadow would look like a tiny speck. Flathead lake does get very deep, but the spot the picture was taken in is fairly shallow. You can’t see the bottom in the deep parts. However, having freakishly clear water means you can see exactly where the sandy bottom drops off into blackness, so this still ranks a 5/10.

The Lower Congo River, multiple countries

Most of the Congo is a pretty normal, if large, River. In the lower section of it, however, lurks a disturbing surprise: massive underwater canyons that plunge down to 720 feet. The fish that live down there resemble cave fish, having no color, no eyes, and special sensory organs to find their way in the dark. These canyons are so sheer that they create massive rapids, wild currents and vortexes that can very easily kill you if you fall in. A solid 6/10, would not go there.

Little Crater Lake, Oregon

On first glance this lake doesn’t look too scary. It ranks this high because I really don’t like the sheer drop off and how clear it is (because it shows you exactly how deep it goes). This lake is about 100 feet across and 45 feet deep, and I strongly feel that this is too deep for such a small lake. Also, the water is freezing, and if you fall into the lake your muscles will seize up and you’ll sink and drown. I don’t like that either. 7/10.

Grand Turk 7,000 ft drop off

No. 8/10. I hate it.

Gulf of Corryvreckan, Scotland

Due to a quirk in the sea floor, there is a permanent whirlpool here. This isn’t one of those things that looks scary but actually won’t hurt you, either. It absolutely will suck you down if you get too close. Scientists threw a mannequin with a depth gauge into it and when it was recovered the gauge showed it went down to over 600 feet. If you fall into this whirlpool you will die. 9/10 because this seems like something that should only be in movies.

The Bolton Strid, England

This looks like an adorable little creek in the English countryside but it’s not. Its really not. Statistically speaking, this is the most deadly body of water in the world. It has a 100% mortality rate. There is no recorded case of anyone falling into this river and coming out alive. This is because, a little ways upstream, this isn’t a cute little creek. It’s the River Wharfe, a river approximately 30 feet wide. This river is forced through a tiny crack in the earth, essentially turning it on its side. Now, instead of being 30 feet wide and 6 feet deep, it’s 6 feet wide and 30 feet deep (estimated, because no one actually knows how deep the Strid is). The currents are deadly fast. The banks are extremely undercut and the river has created caves, tunnels and holes for things (like bodies) to get trapped in. The innocent appearance of the Strid makes this place a death trap, because people assume it’s only knee-deep and step in to never be seen again. I hate this river. I have nightmares about it. I will never go to England just because I don’t want to be in the same country as this people-swallowing stream. 10/10, I live in constant fear of this place.

Honorable mention: The Quarry, Pennsylvania

I don’t know if that’s it’s actual name. This lake gets an honorable mention not because it’s particularly deep or dangerous, but it’s where I almost drowned during a scuba diving accident.

The Gulf of Corryvreckan is indeed in a movie. It’s in I Know Where I’m Going! from the genius partnership of Michael Powell and Emeric Pressburger.

I am currently building a multi-decade chain of papers on computer vision and image generation to have this discussion, and I would like to do so in a moderately calm manner.

Unfortunately if I want to cite a journal article from 1983 in a tumblr post, the copyright holders of this article about image restoration and pixel randomization want $248 per section of excerpted text (<500 words).

This is a 2024 paper by one of the Adobe developers who worked on those compositing tools going over how the tools work. For the record, this model was trained on licensed and public domain images only and the tools are run on device, so the copyright and environmental complaints that people frequently make about AI don't apply to these specific tools.

That paper uses research from this 2015 paper on AI detection of composited images. That paper uses the ImageNet dataset.

ImageNet's dataset is a combination of images sourced from image searches starting in 2009 and description tags generated by Amazon Mechanical Turk workers. The images are not owned by ImageNet, but were scraped from various internet sources in the late 2000s.

That project uses algorithms and processes described in this 2007 paper on the utility of a general image database and image segmentation.

This 2000 paper on image segmentation developed some of the models used in the later paper, based on a collection of 1000 Corel stock images.

That segmentation was based on statistical models in this paper from 1994, which used a small training dataset collected by the researchers.

That 1994 paper made use of the model in this foundational paper from 1984 on predictive pixel selection for algorithm-based image restoration.

That paper helped to refine the boundary-finding methods used in this 1986 paper, which was an improvement on methods from the 1980 Hildreth paper.

Both Hildreth and Canny cite this 1971 memo from the MIT Artificial Intelligence lab, which describes the process of using a computer to find lines in an image.

Personally I like this note from a revised version of that 1971 memo that shows that we are still currently dealing with some of the same problems in computer vision that people were 50 years ago.

"This program has no idea what a reasonable line-drawing should look like when it represents an image of polyhedra. Instead it is very general and will find arbitrary line-drawings. Observing the particular way in which things sometimes go wrong, one quickly comes to the conclusion that higher-level understanding of the scene being analysed could greatly improve the line and vertex creating phase of this program. As thing stand now this understanding comes only after the line-finder has done its work."

Okay so what's my point?

My point is that there is a long chain of models, statistics, and research that, stretching back to the beginning of computer vision, has been centered around figuring out the likelihood that one pixel next to another pixel is black instead of white. The computers have never been very smart, they have never understood context, and the improvements we have made from the line finder in 1971 to "harmonize" in photoshop in 2026 is a very traceable chain of refining how the statistics are calculated.

They hallucinate extra elements, they don't know what shadows are.

Computers are still stupid, they just do math a lot faster than they did in the seventies.

Harmonize is apparently a new "AI" tool available in photoshop that is capable of matching lighting, texture, and other qualities in photos that are composited together. One of the things that the flesh pit guy is currently being dragged for (aside from really seeming to be a pretty tremendous asshole) is using this particular type of AI tool as a time saver on his ongoing art project.

I want to have a conversation about this, by which I mean I want to make some arguments about this.

I'm writing this specifically about the harmonize compositing tool in photoshop (and similar tools like upscaling). This is not about using grok or generating whole images or whole elements of images, this is not about whether or not the flesh pit guy is an asshole. I will grant that he is an asshole and I personally find whole images generated with AI tools like grok to just kind of look bad and be really boring.

Work with me on this, and let's accept the premise that adobe's Firefly is an ethically trained model (up to 5% of the images used to train firefly may be midjourney images that were licensed to adobe as stock images after being generated by midjourney, but adobe pays creators standard licensing fees to train data and does not train on client data), and that the tools are run on-device and do not consume any more resources than creating a 20-layer document in photoshop would.

(Also I acknowledge that training a model uses a lot of power and resources but creating a video game or an animated movie uses power at a much higher level than playing the game or watching the movie on your own machine; i've got to limit the scope somewhere so I can actually ask the question I've got)

Again, we are granting the following before you respond to the poll:

If the model was trained on ethical data and

If the tool runs locally and does not use more power than your computer uses and

The art the tool is used on is a personal, individual project and does not lead to poor treatment of workers

Direct from cellphone, Autofocus

Posterize (edge-finding like the tools in the original linked master’s thesis)

HDR Toning (photorealistic high contrast preset)

Selective Color Adjustment

Glowing Edges (again, another edge-finding tool)

Rendered lighting effects (spot, high intensity, high angle)

Color halftone

Gaussian Blur applied to the color halftone (similar to how AI upscaling works, and is based on the 1986 paper listed above)

Selective color replacement

Cutting backgrounds 100% with tools that use the AI included in my 2012 CS6 license

I think the last two images here and the edge-finding images do a really good job of demonstrating how AI/Machine Learning image processing tools are statistical tools.

The cut-out on the left was done hastily with a magic wand selection tool with a color selection threshold of 20. What that means is that you click on the image and the tool picks all of the pixels that are contiguous with the pixel you clicked on and are within the tolerance. If you broke down the image pixel by pixel you'd be able to get the exact count of what percentage of the image is what color, you could, in theory, remake the image as a barcode of just the proportions of each color using this kind of image analysis. The image is math, the computer sees it as math, the algorithms see it as math, the models see it as math.

The cut-out on the right demonstrates how the "AI computer vision" makes educated guesses (predictions) about the image. That was cut out using a magnetic lasso tool, which you guide around the vague shape of the image with your mouse. The program creates a selection line that follows color variations in the edges of the image that you're cutting out. On a very high contrast background (so if I was standing against a white wall) this can be very accurate. On a lower contrast background, there's more variation as the program guess wrong about which way to move the selection line.

The program doesn't see the photo as a picture of a person being cut out, it sees a collection of pixels of various colors and calculates what the probability is that a pixel of one color is likely to be next to a pixel of another color based on the the difference in color of the pixels.

I could create every single one of those 21 variations using curves or the hue, contrast, saturation, and lightness sliders. But I could also pick the one that looks closest to what I want if I'm in a hurry.

Clout with whom????????? 😭😭😂😂

“When he died the ravens sat in the trees outside for a week, watching. Taking turns. A horde of raccoons tried to break into the house every night, tearing at the siding. Eventually they gave up, but it was unsettling.”

(I'm going to copy paste some of the below from a similar conversation I had the other day so I apologise if there is anything that seems slightly off topic)

I think that the primary sticking point is that your description of TMA/TME primarily comes from the way that (trans) radfems and other bigoted people use it, instead of the way it's primarily used by the majority of people, especially trans women. An analogy I'll draw is your response feels like saying that feminism is bad because TERFS are bigoted. It's rlly important to remember that the people using TMA/TME to attack and put down trans men aren't the majority, and instead are a loud minority, and judging all of transfeminism and it's terms based on this minority can quickly tend towards bigotry.

As for TMA/TME itself (this is the part copy pasted):

Also I do kinda agree about your point about everyone being affected by transmisogyny, and in legitimate transfeminist discussions I do find that the name to be a bit off of what it's really trying to discuss. Realistically all the term exists for is because, for example, in traditional cisfeminism it's easy to discuss the concept of "women are the targets of misogyny". But when it comes to transfeminism it gets a little murky. Because just saying "trans women" excludes transfemme enbys among others. And while there are potentially broader terms you could try to use to encompass everyone, they can quickly become convoluted, and really the point is to discuss everyone that transmisogyny is targeting.

So when you actually approach tma/tme from a transfeminist perspective of trying to engage in genuine discussion of opression, it makes more sense, even if the words are a bit off.

I am truly sorry the way that these words have been used against trans men, and I can understand why you feel the way you do, but I really think it's important to not let a few bigots poison legitimate discussion. It's part of whats leading to the growing rift in the trans community, social media pushing the loudest cruel people to the surface, and people generalising entire groups (especially trans women) based on that.

So, I do somewhat agree that the original use of TME/TMA could potentially have been useful in discussions alongside other, more specific and more nuanced terms, because on its own TME/TMA is simply too binary to encompass the actual lived experiences of all trans, enben and intersex people, but acting alongside other terms, it could have been useful in discussions about transmisogyny and how it effects different people (although I’d still take this with a grain of salt, because as you say yourself, the language is still too rigid no matter what context it’s used in).

However, I disagree with the analogy used here, because I don’t think the two are comparable. I do believe that transfeminism itself has been hijacked by a loud minority, and does not reflect the wider transfeminist community (of which I have been a part of for many years, so I do have an idea of what I’m talking about here. This isn’t just a case of me looking at a few tumblr posts and deciding what I think TME/TMA means). But, when it comes to the actual TME/TMA acronyms, an entirely separate, exclusionary movement has essentially arisen from them to the point where they can no longer be used in a vacuum without acknowledging the wider harm that has already been caused.

Personally, I believe a lot of transfeminist discussions have been set back by the ideology surrounding the TME/TMA binary. To give an example, I sometimes see trans people (particularly trans men) use the term “female socialisation” to describe their experiences growing up in a society that viewed them as a women, and how that connects to their manhood. The actual crux of this discussion is still an important one to have when talking about things like reproductive rights amongst trans men that cis men aren’t affected by, but the actual term “female socialisation” cannot be used in these discussions because it is far too associated with TERF ideology and excludes the nuances and perspectives of how trans women are raised in a transmisogynistic society. In my opinion, I would not trust anyone who uses this term without being wary of what other beliefs they may hold.

Some of these trans people will be using it sincerely because they simply don’t have another term to put into words what they’re trying to explain, but that’s symptomatic of discussions that have not had the necessary time, space or input from other trans and intersex people to evolve better and more inclusive language.

I'm reminded of a time in high school during student body elections, when some of my friends began posting signs around the school to encourage students to write in a vote for the pokemon blastoise. And no one really took it seriously until the school did a psa over the morning intercom not to vote for blastoise and to take the election seriously.

After that everyone in the school, now realizing they had the ability to actually mildly disrupt the proceedings of those in power, agressively and obsessively began campaigning for blastoise. 'A vote for blastoise is a vote for blastoise. Vote Blastoise.' Became the most common thing to hear in the hallway. Some people even made tshirts with a tshirt press. Eventually a human was elected but only because the school didnt count the blastoise votes. Real voter fraud happening here. I bet the boring human class president only got like 3 votes in total but I'll never know how much my hero Blastoise was cheated.