vunderba

Salvador Dalí’s Pinball Thrills could also have been the title of the pinball segment on Sesame Street back in the day.

Every sport ever in pong form was completely true. Magnavox Odyssey Pong + a crappy blue cellophane transparency overlay == ice hockey!.

Anyone remember Slalom for the NES?

https://imgpb.com/hvszd

But third parties definitely didn't respect this. I'm sure everyone who grew up in this era remembers the infamous Megaman cart by Capcom [1].

One of the things that Nintendo learned from Atari was that massive amounts of shovelware did not help their cause.

As a third party company, you were actually limited to a certain number of games you were allowed to release per year in North America (five I believe). There were ways around that, like Konami forming the subsidiary Ultra.

Additionally they locked the system behind the 10NES chip that made unauthorized game production difficult (unless you pulled the classic Taiwan voltage snap trick or just blatantly copied the source from the US Copyright Office like Tengen).

[1] - https://imgur.com/a/5hUUDnM

Buffered controls, different pathfinding AI for each ghost, level transitions, etc.

Gpt-5.6 sol high technically completed the assignment as well, but the gaps within the pipes used to construct the maze, the lack of pause when eating a ghost, etc made it feel far less polished.

- parallax, which might be some clouds moving in the background.

- weather effects which is mostly just an overlay

- animated sprites that interact with, or at least move along, the different sections of the static scene.

It'd be neat to see how well it could recreate some of the larger set pieces like Westminster palace.

I've definitely seen some pretty impressive automated AI voxel stuff (and that was 6 months ago) and that's having to deal an extra dimension. [1]

Personally I still don’t see a situation in which you could do it completely hands-off though. I think you’d end up with some pretty good scenes and some really terrible scenes requiring post-adjustments. It would be an interesting experiment at any rate!

[1] - https://github.com/Ammaar-Alam/minebench

There's definitely some manual curation. They even have assets that are clearly being placed into the scene, like the boats moving in the water.

You could theoretically generate all possible animated sprites (birds, people, vehicles, etc.) for use in any given cityscape, but automatically segmenting/detecting the different valid "paths/waypoints" where those assets should be placed would probably end up looking pretty janky.

https://safaelmali.gumroad.com/l/lofi-cities-complete-collec...

I think most people would agree that early 3D games (PSX and N64 era) - particularly ones attempting to achieve texture-mapped photorealism, like FF7, GoldenEye, Tomb Raider etc. did not age gracefully, at least where graphics are concerned.

Contrast that with something like Mario 64, which, while still chonky, looks way better because it’s so highly stylized.

Screen mode 13h was definitely fantastic. Being able to light up a pixel by mapping directly to the 64k contiguous bytes in the address space was very intuitive.

And yeah dealing with Win32 stuff definitely came with its own set of complexities, I practically had the Charles Petzold Programming Windows book memorized back in the day.

But let’s also not forget Visual Basic, which outside of maybe Delphi, I'd consider one of the greatest RAD environments ever created. Being able to just grab a button onto a form and then double-click it which would instantly create a handler that you could add your logic to was very approachable even for younger audiences.

The only problem of course is that Visual Basic wasn’t bundled with Windows, so it was a lot less accessible than QBasic which came bundled for free with MS DOS 5.0.

  OPEN "COM1:2400,N,8,1,BIN" FOR OUTPUT AS #1 

For example GET/PUT had this one:

  SCREEN 1
  DIM Box%(1 TO 200)
  x1% = 0: x2% = 10: y1% = 0: y2% = 10
  LINE (x1%, y1%)-(x2%, y2%), 2, BF
  GET (x1%, y1%)-(x2%, y2%), Box%
  DO
      PUT (x1%, y1%), Box%, XOR
      x1% = RND * 300
      y1% = RND * 180
      PUT (x1%, y1%), Box%
  LOOP WHILE INKEY$ = ""

As I mentioned in another comment, I think if you didn’t grow up with it, you just underestimate how amazing QBASIC was as an IDE, a programming language, and perhaps most importantly as an educational tool. I mean, it even came with actual BASIC programs (GORILLA, NIBBLES, etc) right there that you could use to explore and tweak the values as you learned.

There’s no better way to learn how something works than to physically play with the actual values, change constants, change variables, and observe how that affects the behavior of the game.

Thanks for adding the little touches like letting me click a tile and have it displace the entire row to the left/right, and also the quotes underneath the tiles is pretty great too!

There’s a huge difference between a kid turning on a Commodore 64 or an Apple II and being dropped into the relatively no-frills versions the BASIC interpreter versus a kid in the early ’90s who had a DOS machine with MS-DOS 5 bundled with QBasic which IMHO is a more representative example of a “batteries included” BASIC environment that was significantly more user-friendly.

I also encountered QBASIC as an elementary school kid - it felt like the closest thing to sorcery. People who grew up in the age of the internet really underestimate how awesome it was to be able to just be able to place the cursor on a more obscure command (like PEEK/POKE or POINT) and see something like this:

  Returns the current graphics cursor coordinates or the color attribute of a specified pixel.

  'This example requires a color graphics adapter.
  SCREEN 1
  LINE (0, 0)-(100, 100), 2
  LOCATE 14, 1
  FOR y% = 1 TO 10
      FOR x% = 1 TO 10
          PRINT POINT(x%, y%);
      NEXT x%
      PRINT
  NEXT y%

https://imgur.com/a/dNS5r5X

For the past 6 months I've also been working on a period-accurate QBASIC 1.1 simulator (and corresponding virtual hardware layer) that runs entirely in the browser.

I've got a stack of books on my desk from when I was a kid in the 90s that have literally been my religious texts (Undocumented DOS, The Programmer's PC Sourcebook, etc), so I know how hard it is to get this right.

I've actually posted about it a couple times here in HN.

Anyway small bit of feedback:

Technically if you don't clear the screen (using CLS, or set a SCREEN mode other than zero), the cursor retains its position even between program executions.

So basically if you have a program like this:

  PRINT "WHY IS A RAVEN LIKE A WRITING DESK"
  END

  WHY IS A RAVEN LIKE A WRITING DESK
  WHY IS A RAVEN LIKE A WRITING DESK

* Your definition of "fun" may vary.

It's definitely a nice upgrade from the last open-weight version (Qwen-Image 1.0) at least in terms of strict coherence and prompt understanding... but a lot of the outputs seem distilled for lack of a better word - likely trained on poor synthetic data. There's also some elements of tinging that very much reminds me of early gpt-image outputs.

You can mitigate it a bit using better samplers (like res_2m paired with the beta_57 scheduler, etc). A lot of people have also seen better results using a higher CFG than what is recommended by the Qwen team.

On my GenAI Image Showdown bench which emphasizes adherence to prompts, Qwen-Image 2.1 clocked in at 7 out of 15 which is SOTA for an open-weight model (Ideogram 4 is the only other open-weight model that outscored it), but the quality is frustratingly inconsistent.

https://genai-showdown.specr.net/?models=local

It's like a Choose-Your-Own-Adventure dialed up to 11.

I played around with it briefly on Fal.ai generating a cartoon, but it’s more of a technical curiosity for the time being since I don’t have a Scrooge McDuck vault of gold coins.

I'll likely be redoing that particular bench with added minimum passing criteria of an anvil.

[1] - https://huggingface.co/spaces/ArtificialAnalysis/Text-to-Ima...

https://www.yachtclubgames.com/blog/the-art-of-the-game

Qwen-Image 2.1 is definitely a pretty big leap over the last open-weight version, Qwen-Image 1.0, released back in August of last year and managed to score 7 out of 15 as opposed to its predecessor which scored 4 out of 15.

Even though it's significantly smaller, 7b vs 20b, it's multimodal (so you don't need a separate image-to-image model like you did with Qwen-Edit), more coherent, and significantly faster even when outputting at higher 2K resolutions. However, in my testing, I found that I had to play with dialing up the CFG depending on the complexity of the prompt.

I've also added a progress dropdown under Model Performance so you can see how cloud vs. local models have been trending since 2024. Spoiler: June of this year released some of the biggest bangers (Krea 2, Ideogram 4, and the kind of slept-on Boogu-Image 0.1).

Downsides:

- It was clearly trained on at least some level of synthetic training data, and it shows in some of the subpar outputs in terms of fidelity. Some of this you might be able to iron out with a refiner model downstream or a custom LoRA but time will tell.

- They've moved away from the permissive Apache license. Commercial usage is only allowed by request.

Comparisons:

https://genai-showdown.specr.net

If you just want to compare local models only:

http://genai-showdown.specr.net/?models=local

A lot of people were putting ZiT as a refiner downstream in early Qwen-Image 1.0 workflows, so I'm wondering if we're going to see something similar with 2.1.

Even though I prompted for a crucible in the prompt, I think the fact that the prompt also contained terms like “blacksmith” and “hammer,” caused it to lean towards anvils over crucibles in some of the pictures (which as you brought up makes more sense anyway).

The three benchmarks it failed on (D20, Flat Earth, and Banded Snake) are pretty difficult, so I'd be surprised if 2.5 manages to pass them, but I’ll add it for completeness’ sake later this week.

I’ll have to think about this one. When I crafted the prompt, I wasn’t really thinking about the differences between a crucible and an anvil. It was more the visual of an archangel smelting halos for newly arrived heavenly beings.

Positives

• It's a heck of a lot smaller than Qwen-Image 1 (20b parameters) at only 7b, making it one of the smaller open-weight models available (Z-Image Turbo is one of the few that is smaller at 6b) when compared to Ideogram, Krea2, Flux2, etc.

• It supports native transparency (Qwen's team, as far as I know, is the only one attempting to tackle this). Even though it's relatively trivial to set up background removal postprocessors, it's also neat to see it natively supported.

• It's fast using QwenImage2.1 convrot, a 1MP image took around ~5 seconds on an RTX4090.

Negatives

• The license (assuming you respect it) is far more restrictive. The original Qwen Image 1 was released under the standard Apache license; this one explicitly forbids commercial usage without obtaining a separate license. On the other hand, a lot of us didn't expect the Qwen team to ever release "weights-available" ever again.

Qwen-Image 1.0, released about a year ago, only scored 4/15 on my GenAI Showdown Benchmarks. Since that time, they've been upstaged by Krea 2 (6/15) and Ideogram4 (8/15). I'll post the new results once I have some more time to run them.

https://genai-showdown.specr.net

And Krea 2 has a community license [2] that is fairly permissive - I think commercial usage is allowed under $1 million.

Boogu-Image scored 6/15 and Krea 2 scored 7/15 on my GenAI Showdown benchmark [3] - only Ideogram4 eclipses them in terms of local models, but its got a far more restrictive license and the JSON structured inputs can be a pain to work with.

[1] - https://github.com/Boogu-Project/Boogu-Image

[2] - https://www.krea.ai/krea-2-licensing

[3] - https://genai-showdown.specr.net/?models=fd,hd,kd,qi,f2d,zt,...

• Wish this was a web app though, I have a general policy of not isntalling unknown software on my computer.

• Website needs a demonstration video (or at the very least some screenshots)

If you're interested in improving your blindfold chess techniques, I actually put together a pretty in-depth blindfold chess trainer website about a year ago called Shah Kur. It lets you play Stockfish AI with different blindfold variations (Last N hidden, pieces reduced to color only, etc.), and it uses voice activation/TTS so you can play on BT headphones while you're out walking without having to physically look at your phone.

It also has a positional recall game constructed from historical games, so you’re building an actual practical “memory chunking” perspective instead of just random pieces on a board that would never correspond to an actual game.

https://shahkur.specr.net