Amphobet

1. Bulbasaur

Understood the assignment. Overall basic idea of bulbasaur has been expressed. Spot placement is loose and generalized. 3/4 of them do not have fangs. Some of their eyes are not the right color. All of them have pupils, which is not a trait found in Bulbasaurs but I'll allow it for the style that they are using. As a cute bulbasaur render, it passes.

Grade: B+ (probably nightshade your bulbasaurs)

2. Ivysaur

Is slowly starting to lose the plot. Most of the time, the ivysaurs generated by the algorithm are either bulbasaurs with buds, ivysaurs with bloomed flowers, or an in-between of ivysaur and venusaur. Flower isn't even the right kind. And some of them become bipedal with tails?? the fudge? And there are too many flowers in the background. The composition is starting to become cluttered.

Upon giving it the bulbapedia description of its physical appearance, it was a little more accurate. However, the leaves are all wrong and it still suffers from too many spots syndrome. One even had really thin pupils.

Grade (without full description): D Grade (with full description): C (you probably don't need to nightshade your ivysaurs, but seeing the next pokemon... yeah you should probably do that.)

3. Venusaur

Horrible. Absolute failure. This is just a bigger bulbasaur with ivysaur's colors and venusaur's plant.

With description is even worse. Nice rendering, but as a representation of Venusaur, it fails spectacularly. Still a bunch of Ivysaurs. With too many spots. And none of those flowers are remotely accurate.

Grade: F (for both of them. Venusaur fans, you are safe. Bulbasaur and Ivysaur fans, though? Nightshade them to hell and back.)

4. Charmander

Proportionally it needs to a be a little thinner, but other than that? Very scarily accurate, random Pokémon gobbledygook not withstanding.

Grade: A (nightshade your charmanders)

5. Charmeleon

Asked for Charmeleon, ended up with some bulbasaur/charmander/charizard fusions. Which is nice, but its not what I asked for. Failed automatically.

Is better with the physical description, but it still has some issues. It's not the right color of red, some of them are quadrupeds, and there are dark greyish brown spots which the description did not have. The cream scales also extend to its mouth, which is also not what the original charmeleon had. Points for originality (well, as original as an algorithm that scrapes images can get), but this is still not going to get a high grade.

Also nice crab claw flame.

Grade (without description): F

Grade (with description): C-

6. Charizard

Also understood the assignment. Aside from the flaming tail and some wing bone coloring issues, this is a really accurate representation of a Charizard. It sometimes fails in the proportion department, but 9 times out of 10 it poops out a charizard that doesn't look janky. Though considering that Charizard is one of those really big Pokémons, of course its going to get that right.

Grade: A+ (Nightshade your charizards)

7. Squirtle

If it wasn't for the machine's struggle with the tail, we would have another A+ on our hands. Which is a scary thing to think about.

Grade: A (Nightshade your squirtles)

8. Wartortle

The one time it actually got Squirtle's tail right, and it was in the section where the AI struggles to generate a Wartortle with only its name to go by. Just a bunch of bigger squirtles that sometimes go quadrupedal and have blastoise ears.

With description is slightly better, but it still fails. All of them are quads, some of them have blastoise mouth, and one even has a mane. The tail isn't accurate either, but then again the cohost designer has a character limit. Even without a character limit, I'm still gonna grade it negatively. Especially since it has ignored the bipedal part of the description.

Grade (without description): F (seriously. nightshade your squirtles.)

Grade (with description): D

9. Blastoise

Appears to understand the assignment, but it only understands the overall body plan. We got tangents and multiple guns galore. And Blastoise.... holding guns?? The fu-?

Also, Dalle 3 does not know how to pixel art. Pixel artists, you have been spared.

With description, it fairs a little bit better... from a distance. 3/4 of the blastoises have malformed hands, the white shell outlines do not wrap around the arms like a backpack, (which some of the gun toting blastoises actually got right!) and one of the images' ears are too big.

Grade (without description): C-

family reunion!!

yippee part 4! i decided to split the original script in half just because it was way to long. see you in part 5 :-] !

Source

for those unaware, the spaghetti wall of letters and numbers is a base64-encoded JPEG image (and not a URL as some guessed). in certain cases when you tried to insert/paste an image into what’s ostensibly a text-only box, this could happen.

the thing that’s bugging me however is that there’s image data there. we have fairly a clear (albeit with JPEG artifacts) screenshot of text that, thanks to how Windows ClearType renders text, each character is identical to each other, that is to say, an uppercase Q will always look more or less pixel-perfect each time, meaning we don’t have to guess what a Q looks like, we simply have to pixel-accurate match it.

as an aside, this is why regular OCR struggles so much with this kind of data retrieval, such as code even when it’s clearer than a physical paper scan. ordinarily, OCR will try to best-guess every single letter because it expects each letter to be slightly different from each other (as would be the unpredictable nature in a scanned document), and on top of that most OCR today will try to autocorrect because it expects the scanned text to contain words in some human written language.

so, all we have to do is make a program to recognize each character and piece back together the whole base64 string, right? well…

first i stitched all 7 images back into a single block of text, observing the consistency of the line spacing. some of the screenshots have little bits of the previous one sticking out of it, which helps with alignment and to make sure they’re in the right order.

after that i had to sample every single letter off this file. this means going around the file and finding one example of each different character we’re trying to identify, saving it as its own separate file so that the program can load them as references to compare against in the full image. for base64, the alphabet consists of a-z, A-Z, 0-9, +, / and =. once i had the initial code in place…

…close! but oh so far. if any one single character in a base64 string is wrong or missing, the resulting decode will be wrong. the issues i was having were mostly with the lowercase r and j because of how the kerning affected the pixels around those letters. i was also getting false matches for r where there should be an m. what followed was grueling hours of tweaking the matching code and my known font set to better fit the original image and get as close as possible to a 100% match. here is the resulting code, maybe it’ll be useful for someone and this won’t have been a complete waste of time.

once i was confident through the verification image that i had all characters recognized, i put it through a base64 to JPEG decoder. i actually did this several times as i improved the recognition and what follows is the best result that came out of it yet. i suspect some of the data might be missing (perhaps a line or block of text got lost in between screenshots), or i have a wrong character somewhere resulting in a wrong value. this is the image extracted from OP’s base64 string:

with this it still doesn’t parse as fully valid base64 in strict mode so i think there’s still another letter in there that’s wrong, but i couldn’t find it. however this gives us a better look:

and this is finally enough to do a reverse image search. i present to you, the HD version of our intrepid massive backpacker: